HPE Vertica holds an enviable position in the big data world, an early entrant with one of the largest installed user bases and a leader in MPP architecture, it has quickly risen to the top tier of EDW technology. The column-oriented, highly scalable analytical database was built to address the most demanding big data analytics initiatives. There are plenty of proof points of HPE Vertica analytics capabilities. One of the most advanced SQL databases, HPE Vertica supports high degree of concurrency and parallelism, advanced compression capabilities, cloud integration and In-Database machine learning resulting in enhanced performance at scale.

Businesses today, such as social media giant Facebook, ICICI Bank, Aircel and MTS, among other leading heavyweights, rely heavily on HPE Vertica 8 to answer some of the most business-critical questions. The platform gives the businesses the capabilities to conduct sophisticated analysis at “industry-leading” scale and speed, regardless of where their data lives.

The database management market is rife with competition and HPE has a definitive lead in big data platform vendor list dominated by Oracle, IBM and Teradata. To add to that, HPE has a clutch of big name customers [Cardlytics, Chase, AT&T, WebMD] in its corner to stave off competition from big data vendors and cloud-based analytics service providers AWS, Azure and Google Compute Engine. With flexible deployments, HPE has targeted the right customers who don’t want to control their infrastructure and not ship all the data into the cloud. The strategy seems to have paid off well.

Vertica is a mature solution, designed for complex, query-intensive applications and has a definitive edge over Hadoop-SQL combinations. With user-friendly features being the name of the game, HPE Vertica is definitely going where its customers are and solving performance challenges that business environments face today. For years, it was all about having more and more data but gradually, there are more concurrent users and competing, simultaneous workloads going on.

Click here for more details on HPE Vertica.

The post Why HPE Vertica 8 is the best fit for businesses to create an analytics-driven enterprise appeared first on Analytics India Magazine.

We believe AI is a new scientific infrastructure for research and learning that professionals will need to embrace and lead, failing which, they will become irrelevant and eventually redundant. When applied in science, AI can autonomously create hypotheses, find unanticipated connections, and reduce the cost of gaining insights and the ability to be predictive.

The human mind processes millions of sensory inputs automatically and constantly, making it the most elegant computer in existence. But the human brain only contains about 300 million pattern processors that are responsible for human thought. What if we could bring to existence all the ideas not just with data, but also orders of magnitude more data processing capability?

In the name of AI, what we have now is narrow AI. But the pace at which we are moving towards general AI, which would outperform humans at nearly every cognitive task, cannot be ignored. However, despite being at a nascent stage, AI has already enough contribution in the field of banking, medicine and security among others. And while experts continue to work to make AI more advanced, one of the biggest challenges they are facing is the lack of skilled manpower.

Why Is There A Need To Upgrade Your Skillset? 

In 2017, the global AI market is expected to be worth approximately $2.42 billion, which will grow to be worth $18 billion by 2022. Globally, investors backed more AI companies in the first quarter of 2016 than in any other quarter, while more than 200 AI-focused global companies have collectively raised more than $1.5 billion so far, this year.

While there has been enough clout around the rising demand and need for data scientists, there has been several institutions that has introduced courses to adept professionals with data analytics. However, what still remains unexplored are professional courses designed around AI, which needs a completely different skillset.

With very few institutions venturing into the AI arena, interestingly, the Times of India Group understood the need for it and designed a professional course to help candidates understand and train in AI.

TimesPro, in technical partnership with NVIDIA- the AI computing company, has introduced three comprehensive courses to enable professionals to navigate the world of decision science.

The three programs are designed in a way that it can help professionals from all levels to upgrade themselves. Regardless you are someone who is an entry level manager or belong to the mid-senior or senior level, the programs equip participants with skills and knowledge to influence decision making, strategy.

The programs have been planned in a way that professionals can enrol without having to quit from their present jobs. Moreover, TimesPro also provides placement assistance to candidates, helping them find better opportunities soon after the course. It also focuses on the understanding of latest analytical concepts like descriptive, predictive and prescriptive analytics to solve business problems.

A Brief Look at The Programs:

• PG Program in data science (PGDDS): This program is specially designed for entry and middle level managers with minimum 2 years of work experience (desired experience is 4-5 years), who seek career advancement via specialised training in Business Analytics/Data Sciences applied to their field
• PG Program in advanced data science (PGDADS): The second program is for entry, middle, senior level managers with minimum 5 years of relevant work experience. The course is apt for someone who is seeking career advancement via specialised training in advance data sciences applied to their field
• Certificate in advance decision science (CADS): The third and the last course is designed for working professionals with relevant experience in data analytics and big data experience, who apply quantitative techniques to make effective decisions.

Why Should You Opt for This Program?

Besides the fact that the course enables you to upskill yourself and makes you a sought-after employee, the best part is the course has a technical tie-up with NVIDIA, a company, which is synonymous with artificial intelligence.

Moreover, its GPU- accelerated computing with a CPU is used to accelerate deep learning, analytics, and engineering applications. Pioneered in 2007 by NVIDIA, GPU accelerators now power energy-efficient data centres in government labs, universities, enterprises, and small-and-medium businesses around the world.

While the chip-maker is likely better-known for producing high-end graphics to power pixel-pushing gameplay, it also started providing the hardware and software that could help bring about the future of driving.

NVIDIA has already forged self-driving alliances with automakers Audi, Toyota and Volvo and has a partnership with Chinese internet giant Baidu to bring artificial intelligence to cloud computing, self-driving vehicles and AI home assistants.

Why TimesPro?

The Times Group has evolved into a multi-media conglomerate focused on providing solutions to consumers across the nation as well as creating a better tomorrow for the Indian youth.

Anish Srikrishna, President, Times Professional Learning (TPL), said, “We are delighted to be associated with NVIDIA. TPL aims to provide quality education, training and skills with strong industry connect and this program with NVIDIA reflects that deep interest and commitment. We believe relationships such as these will be truly beneficial to the industry, ensuring first-day-first-hour productivity.”

Our Review:

The course has been designed keeping in mind the fast changing demands and requirements of the industry. All industries are now beginning to understand the value and need for Machine learning, which encourages a more profound democratization of intelligence, although this is true only for low-order knowledge.

All the three programs are divided into modules, with special focus on all important aspects of AI and machine learning. And the biggest USP is that they are designed in a way that applicants will not have to quit their jobs or even take time off to apply for the course.

The course duration may range between a minimum of 100 hours and a maximum of 380 hours, depending on the program. Moreover, unlike most courses, this is a little easy on the pocket and will set you back by a maximum of Rs 2,50,000 depending on the program opted for.

But over and above the up to date curriculum, TimesPro also provides the most vital features:

• Expert Industry Professionals as faculty
• State-of-the-art Learning Centres
• Holistic Training – Practical’s and Theory
• 100% instructor-led classroom training
• Placement assistance

Last but not the least, the name NVIDIA and Times of India group should be enough to validate the course for its authenticity and effectiveness.

For more details, visit here.

Download the brochure

The post The Rising Demand of AI Experts: TimesPro Collaborates with NVIDIA to Bring One-Of-A-Kind Course appeared first on Analytics India Magazine.

Analytics India Magazine, recently delved into why HPE Vertica 8 is the  best fit for businesses to create an analytics-driven enterprise! [See the article, here.]

Without further ado, here are the top 7 reasons that make HPE Vertica 8 a good fit for every organization:

Reason #1: Vertica 8 has a definitive edge vs Hadoop-SQL combinations that are yet to achieve interactive performance.

HPE Vertica 8 not only provides SQL layer on top of Hadoop but it also supports fast data access to both ORC and Parquet. Vertica can connect and read data from any Hadoop instance. Everyday users rely on SQL query and Vertica 8 allows customers to access files in HDFS stored in ORC, Parquet format and achieve significant performance benefits compared to raw text files.

Reason #2: Expanded Cloud Integrations – Run Vertica in the loud that is relevant to the customer.

The idea behind expanded cloud integration was to “enable users to run Vertica in the cloud, that is relevant to them, without being locked into a particular cloud.” This gives the customer the choice which cloud to run in – with the expanded cloud support, it is about more deployment options, but helping customers manage it well in the cloud. The multi-cloud integration is in line with HPE’s strategy of helping customers accelerate their digital transformation. As part of the latest release, HPE Vertica 8 now supports Microsoft Azure Cloud and the new release also features expanded AWS support with access to S3.

Reason #3: Advanced compression capabilities deliver results at speed and scale.

As opposed to legacy technologies, HPE Vertica 8 has a full-featured analytics system that reduces big data analyticsquery to minutes and even seconds. The power of Vertica lies in parallelism and there are many emerging use cases where customers are finding more ways to use this architecture.  High degree of concurrency and parallelism is at the heart of Vertica’s success and massively parallel processing can handle data at petabyte scale and speed.

Vertica gives a compression ratio of approximately 1:8 or maybe beyond and the benefits are twofold – a) data gets compressed; b) significantly reduces footprint for the infrastructure and reduces the cost. HPE Vertica 8 platform is 50x–1,000x faster than legacy data warehouse solutions and cranks out 10x–30x more data per server.

Reason #4: Expanded analytical database support for Kafka, Spark and Hadoop

Vertica 8’s extensive integrations to Apache Spark, HDFS and Kafka allows users to analyse the data where it resides.  The open format means customers can analyse data as it is without transforming or moving it. Which means that instead of dealing with big batch loads of data every few hours, microbatch streaming has become one of the most popular ways of getting data into Vertica.

Vertica provides Kafka Connector to support real time stream data ingestion from different sources. It also provides Spark connector so Spark applications can read from Vertica database into Spark memory and can be processed along with machine learning algorithm.

Integration with Hadoop to support largest ingestion is a strong spot for Vertica. Vertica provides capability to read and write into Hadoop and can read different files formats from Hadoop like ORC, Parquet, Avro, Json. It also enables users to stores its own data file into Hadoop that makes Vertica flexible to integrate with any Hadoop distribution and act as a fast data processing layer along with Hadoop.

Reason #5: HPE Vertica 8 is the right choice for big analytics workloads.

The new-age analytics platform is specifically designed for big analytics workloads and packs a wide range of analytical functions to support faster decision making. It is suitable for OLAP applications as opposed to OLTP application since Vertica is very fast on data ingestion, querying and analysing patterns. Vertica is packed with inbuilt prediction modelling, sentiment and geospatial analytical capabilities that gives it an edge over other competitors. As part of the latest release, Vertica also has support for native machine learning algorithms.

One of the key advantages of HPE Vertica is that queries run faster — 50–1,000x faster than any data warehouse. Vertica’s in-database machine learning enables users to embrace Big Data and accelerate business outcomes. Under the new release, for in-database machine learning, the parallel machine learning algorithms have been brought inside Vertica, so that users can effectively analyse data and make predictions without exporting it out of Vertica.

Use cases:

• MTS India has made HPE Vertica a key part of their core telecom business and the proof lies in the number. The telecom player leverages the big data platform for driving accurate, targeted customer campaigns to understand how customers were responding to promotions – outcome resulted in doubling the conversion rate. The Big Data implementation at MTS India has significantly brought down the overheads in business operations.
• When ICICI Bank required information on whether they should deploy more ATMs in a particular region or understand the kind of transactions customers were having, they used HPE Vertica Analytics Platform built-in geospatial analysis function to get insights. One of India’s leading banks, ICICI has approximately 4000 ATMs across the country and depending on the geography and population, the bank wanted to ascertain whether they should be set up more ATMs.

Reason #6: HPE Vertica 8 shines on scalability, speed & performance.

It’s been more than a decade since Michael Stonebraker, database pioneer whose groundbreaking idea of an architecture that stores data in columns rather than rows, paved the way for HPE Vertica. Now, its mature columnar storage makes ‘hot data’ available faster than a traditional RDBMS solution and that makes it the right fit for large enterprises demanding high performance and ease of scalability.

Vertica works on Massive Parallel Processing (MPP) concept that provides high performance and scalable architecture on commodity hardware.  It means, nodes can be added on the fly and Vertica does automatic data balance within all nodes i.e. within cluster. The performance automatically increases as you scale out the cluster size. It helps to reduce the cost and further reduces TCO for running the system.

Features like Parallel Load and Apportioned Load options makes data load option faster in Vertica 8. Case in point, a single large file or other single source is divided into segments, that are assigned to several nodes to be loaded in parallel. Vertica also provide option to define target wise loading in advance which makes system understand the loading strategy in advance, thereby yields to faster data loads in the system. The speed and scale at which data ingestion happens in Vertica 8 is phenomenal. Customer sessions reveal the scale at which Vertica operates is mind boggling with users loading 3000 unique tables an hour, each table featuring up to 1500 columns, underscores the volume of data ingestion.

Reason #7: HPE Vertica’s pricing model makes it a good fit for big & small players.

The right big data analytics platform is one which fits the needs of large organizations and SMBs.  HPE Vertica’s pricing model scores big from a cost perspective. Haresh Krishna Kumar Nese, HPE Country Manager – India & SAARCexplains why the HPE Vertica is the best choice for organizations, big and small alike. “SMBs have high requirement for solutions and they need analytics to improve operations and generate new revenue streams. Hence, we have reduced the entry barrier by offering a subscription based model so that smaller players can also adopt HPE Vertica platform and make better business decisions,” Nese stated. The subscription model strategy seems to have paid off well, since most customers worry about a “Vendor lock-in.”

• Licensing Model: Under the perpetual licensing model, customer can invest in a license and get the support features as well.
• Subscription Model: The subscription based model has significantly reduced the entry barrier for SMBs that can adopt advanced analytics database platform by paying a less amount upfront. By removing in a lock-in period, the entry barrier has been significantly minimized.

For more details on HPE Vertica, click here.

The post AIM lists top 7 reasons HPE Vertica 8 is a good fit for every organization appeared first on Analytics India Magazine.

Its not unknown that enterprise technology customers in India are a hard nut to crack. Not just an effective solution to their problem statement, customers in India look for end-to-end engagement and a deeper relationship with their vendors.

Big data management solutions have seen steady adoption in India in last few years. And most vendors acknowledge this fact. Almost all large players in big data solution space have a presence in India. They have beefed up their platform offering to match with customer requirements.

While, it’s a space that almost all players want to conquer, Big data delivery comes with its own set of complexities. We decided to look at 3 customer success stories for HPE Vertica and check on what are those finer points that make them click here. These can carry valid pointers for other players and customers alike.

Check out the top 5 customer success stories

1. Aircel joined hands with HPE Vertica 8 to support unprecedented growth in data

Telecommunication major and India’s fifth largest mobile service provider was grappling with huge volumes of structured data, that grows at 10-15 percent annually. Like most companies, Aircel looked for improved database performance and scalability at a lower cost. What the Gurugram – headquartered telco needed was faster data loading and querying performance. Keeping in mind Aircel’s requirement, HPE created a datamart to support a large customer base and analyze up to 200 GBs of summarized data daily.

According to Sanjeev Chaudhary, what led to Aircel signing up for the HPE Vertica 8 platform was the exceptional POC with great results in data loading, query execution and backup times. “If we expand this in the future, it can be a single enterprise data warehouse source for other data marts with similar workloads. HPE Vertica 8 satisfied our strict requirements,” he said. Given that Aircel’s data size will grow by year by year, HPE Vertica 8, according to them, was the right choice for handling various workloads, processes and queries faster.

Here’s how HPE Vertica 8 scored over Aircel’s previous solution:

• HPE’s support costs were in line with the budgetary goals for a premier data analytical platform
• Addressed short-term needs and long-term data growth projections
• Reduced the company’s total cost of ownership for managing 10-15 per cent annual data growth
• Advanced the company’s objective of providing in-time information for decisions to fuel growth
• Enabled Aircel to explore new opportunities for monetization stream as a GSM mobile service provider

2. HPE Vertica powers real-time data analytics at MTS India

When MTS India wanted to make the most of the information buried under reams of data, it turned to HPE Vertica 8 to architect a solution to identify potential customers and capture them with targeted marketing promotions in a short span of time. The objective was – leveraging real-time data analytics to push the roll-out of competitive incentive campaigns. With stiff competition, characterized by high churn rate in major metro cities such as Karnataka, Gujarat, Tamil Nadu, Mumbai and Deli NCR, MTS India wanted to expand customer retention by gathering usage data from the prepaid customer service customers from IT and network elements.

“Our marketing team was looking for an instant solution for reaching out to customers based on their real-time activity,” said Rajeev Batra, CIO, MTS India. MTS architected its solution using HPE Vertica, which met its marketing requirements and in the telco space in India, MTS was the adopter of HPE Vertica.

Here’s how HPE Vertica 8 scored over MTS India’s previous solution:

Before deploying HPE Vertica 8, MTS India was running a rudimentary system for batch processing of data related to marketing campaigns. There was a 24-hour lag between getting insights from data and running marketing campaigns.

• HPE Vertica implemented for the Advanced Campaign Management, HPE Vertica leveraged customer analytics to determine the right kind of offers to target users
• Vertica was set up within 48 hours and the data loading platform provided rapid data analysis, powering real-time analytics
• Of all the platforms evaluated, Vertica provided a balance of price and performance

3. Big data analytics provider Abiba Systems migrated to HPE Vertica 8

Abiba Systems is a leading provider of AI powered products and solutions and has serviced Fortune 50 clients from the telecom sector. The company’s solution portfolio consists of proprietary products on BI, Advanced Analytics, Crime & Security Intelligence and Campaign Management System.

Abiba Systems wanted to leverage HPE Vertica’s analytics capabilities for Customer analytics, Behaviour analytics, Predictive analytics, IoT, Enterprise Data Warehouse optimization among other solutions. Abiba Systems had used several pureplay and legacy vendors such as Oracle, IBM, Apache Spark before migrating to Vertica. Vertica is deployed for BI, Visualization and ETL tools in their environment.

Here’s where HPE Vertica lends competitive advantage over leading vendors:

• In database analytics capabilities
• Superior compression
• Standard SQL interface
• Advanced analytics — geospatial, predictive and sentiment etc.
• Concurrency for thousands of users

The post These testimonials speak volume on what customers in India are looking for in a Big Data Solution appeared first on Analytics India Magazine.

Each year we come out with our study of Analytics firms in India. The goal is to put numbers into the scale and depth of how various organizations around analytics and related technologies have surfaced in recent years.

Here’s our annual study for this year.

• Read Analytics India Companies Study 2016
• Read Analytics India Companies Study 2015
• Read Analytics India Companies Study 2013
• Read Analytics India Companies Study 2012

Key Trends

• Last year has since the biggest jump in the number of companies in India working on Analytics in some shape and form. More than 5,000 companies in India claim to provide analytics as an offering to their customers. This includes a small number of companies into products and a larger chunk offering either offshore, recruitment and training services.
• There is growth rate of almost 100% year over year in the number of analytics companies in India from last year.
• Moreover, the number of analytics companies in India are still very few in number, compared to the strength of analytics companies around the globe. In fact, India accounts for just 7% of global analytics companies. This is down from 9% last year.

Company Size

• On an average, Indian Analytics companies have 179 employees on their payroll.
• It is an increase from an average of 160 employees since last year.
• On a global scale, this is quite a good number, as analytics companies across the world employ an average of 132 employees
• Almost 77% of analytics companies in India have less than 50 employees compared to 86% on a global level.

Cities Trend

• Delhi/ NCR trumps Bangalore to house the most number of analytics firms in India this year, at almost 28%.
• It is followed by Bangalore at 25% and Mumbai at 16% analytics companies.
• Hyderabad, Chennai and Pune are far behind with their percentages of analytics companies in single digits as reflected in the graphs above.
• However, these numbers seem to have not changed much since last year.

The post Analytics India Companies Study 2017 appeared first on Analytics India Magazine.

In recent years, there are quite a few things that have happened within the Big Data Ecosystem. Firstly, the market got seriously crowded with big data infrastructure vendors. While open source data infrastructure (MongoDB, Apache Hadoop, Apache Kafka, etc.) emerged to provide a valid solution to big data as a problem statement, legacy data infrastructure providers (Oracle, IBM, HPE, Teradata) latched on this opportunity to provide more viable, managed offerings. These legacy players also addressed their top challenges to support new and growing requirements:

• Improved scalability
• Deeper integration with Hadoop and NoSQL
• Better performance

Secondly, this “crowding up” led to an obvious confusion among customers, amplified by the fact that big data is relatively a recent and complex technology. It’s difficult to ascertain which features really matter for certain business use cases.

Key players

The big data vendor landscape can broadly be divided into two kinds of players: the old guard (Oracle, HPE, Teradata, etc.) and the new entrants (Cloudera, Hortonworks, Pivotal, etc.).

It’s important to note here that given how new this technology is, the tradition database players (the old guard) moved very quickly to capture this area. Rightly so, as the demand for traditional database management solutions had plateaued and the advent of big data was seen as a disruption in this space. By our estimates, there are at least 50 players currently in market with enterprise-grade big data offerings.

While early days were marked by vendors adopting and offering built-to-use big data solutions, what we are currently seeing is an all-out battle between these key players. The winner can easily take it all for years to come.

The comparison

We analysed a few big data vendors with an aim to find out what really is beneficial for customers and how each stack up. This is a qualitative comparison and the benefits of each solution might differ based on the customer’s specific use case, industry and other metrics.

Deployment model: Cloud…any cloud

Being available on cloud is a big feature. Most large vendors today have moved to cloud. Traditional players like Microsoft, Oracle, HPE Vertica and Amazon were quick to move to cloud, if not the new entrants.

What differentiates these cloud big data vendors today is being cloud agnostic. MS HDInsight is on Azure, AmazonEMR is on AWS. This is where HPE Vertica scores big.

The idea behind expanded cloud integration is to “enable users to run Vertica in the cloud, that is relevant to them, without being locked into a particular cloud.” The multi-cloud integration is in line with HPE’s strategy of helping customers accelerate their digital transformation. As part of the latest release, HPE Vertica 8 now supports Microsoft Azure Cloud and the new release also features expanded AWS support with access to S3.

Cost

On a global level, traditional database players have built custom hardware around commodity components, and core revenues are derived from huge maintenance costs. With a Draconian pricing model and the vendor lock-in period, these database giants’ supremacy is challenged by a cheaper, more cost-effective option.

Vertica has one of the most of moderately priced licenses, as compared to other players (which also require a great deal of effort in configuration). Apart from big licensing fees from other vendors, most deployments also need large teams to deploy and run. On the other hand, Vertica requires few specialists and configuration requirements can be handled by traditional developers. It’s here that HPE Vertica has made a huge play and is nipping at competitors’ heels with the the most price-effective option.

Moreover, the subscription-based model reduces the entry barrier for small players and it seems to have paid off well in India. This allows SMBs to adopt the platform by paying a lesser amount upfront, and not worry about “vendor lock-in.”

Architecture

Cross-platform big data tools score higher here. And this is where most players falter. New entrants like Cloudera and Hortonworks are tied closely with Hadoop. Databricks is Spark. Vertica 8, on the other hand, has extensive integrations to Apache Spark, HDFS and Kafka as well, which means that customers can analyse data as it is without transforming or moving it.

Vertica is packed with built-in prediction modelling, sentiment and geospatial analytical capabilities that gives it an edge over other competitors. As part of the latest release, Vertica also has support for native machine-learning algorithms. Under the new release, for in-database machine learning, the parallel machine learning algorithms have been brought inside Vertica, so that users can effectively analyse data and make predictions without exporting it out of Vertica. Most platforms today do not offer a built-in data science capabilities.

Closing thoughts

Here are our final thoughts. If you are looking at sheer number-crunching ability of heavy data sets and prefer a SQL database, go for Vertica—as data loads quickly and is best for heavy-duty queries. In many ways, Vertica can be utilized for small and big enterprises alike and it is best for intensive business intelligence. If you want a true columnar storage option, Vertica also has the best analytics platform capabilities, so it is the ideal fit.

Now, with data management headed for the cloud, the DB wars will now be fought in the cloud rather than on premise. Competitors such as Amazon Redshift, Cloudera and Snowflake Computing offer more cloud elasticity.

No matter where we stand today, the course from here on would decide much of big data’s future. Very soon, other players would adopt these features and be competition ready. The space itself might see changes, given all the technological advancement that’s happening so quickly with big data. There’s no clear winner as of now, but that might change very soon.

The post Comparing Big Data solutions—who will lead this highly competitive space appeared first on Analytics India Magazine.

The ACM Turing Award is the most prestigious technical award in the computing industry. Michael Stonebraker is the recipient of the 2014 ACM Turing Award for fundamental contributions to the concepts and practices underlying modern database systems and has been an adjunct professor of computer science at MIT since 2001.

Through a series of academic prototypes and commercial startups, Stonebraker’s research and products are central to many relational database systems. He is also the founder of many database companies, including Ingres Corporation, Illustra, StreamBase Systems, Vertica and VoltDB, and served as chief technical officer of Informix. He is also an editor for the book Readings in Database Systems.

Here’s our exclusive interview with Dr Stonebraker.

AIMAnalytics India Magazine: There’s a constant fear around AI and many experts suggest that it may lead to automation, risking human jobs. What is your take on it?

MSMichael Stonebraker: I think that’s absolutely true and well understood. For example – President Trump has been talking a lot about US jobs being taken away by companies offshoring them to other countries. That’s really not what’s happening at all; it’s automation that is taking manufacturing jobs. And robotics is going to continue to do that. In fact, it’s going to accelerate. I think a very good example of that is self-driving cars. In my opinion, they’re at most a decade away and the early application is going to be long haul truck drivers. I think there are more than a million of those jobs and they’re going to go away.

The other area where it’s going to make a huge difference is predictive analytics and machine learning. We’re already seeing predictive modeling financial planners and they will replace low-end human jobs.

AIM: How do you think the future of AI is going to be? Will it witness a prolific rise or AI winter is near?

MS: I think machine learning and robotics are going to prosper for at least the next decade. There is every reason to believe that there will be no AI winter anytime soon. And I think the previous AI winter was basically the AI guys making claims that didn’t turn out to be realistic and didn’t translate into products. I think this time around, there are very obvious markets. As an example – the machine learning course at Massachusetts Institute of Technology is wildly popular, as it is at Stanford University.

AIM: Since having won the Turing Award, what has been your research focus?

MS: I’ve been working on three different things.

For the first one – Data science is all the rage these days and everyone predicts that data science is going to replace business intelligence in dealing with large data sets. However, most data scientists spend at least 80 percent of their time in data prep – finding data sets of interest, cleaning them, getting access to them, sorting them into common units and de-duplicating them. Most don’t spend more than one day a week doing the job which they were actually hired for. Instead, they spend four days a week on data prep or “data munging.” So, I along with a bunch of others am involved in a research project called Data Civilizer. The idea is to knock down that 80 percent of work that goes into data munging. And in my opinion, that’s the important problem in data science – it’s not the algorithms that actually do the data science, because researchers spend only one day a week doing that kind of stuff.

Number two: Today, computer networking is getting faster more rapidly than nodes in a computer network are getting beefier. In the database world, essentially all database systems have been architected based on networking being the high pole in the tent – that’s the thing you want to worry about the most. But it looks like that is no longer true, and I’ve been thinking about re-architecting, especially data warehouse systems based on networking not being the high pole in the tent.

Number three: For the last 35 years or so, the database community has had a standard way of suggesting how people should do physical database design, which included how to decide what set of tables you’re going to put in a database.

However, it turns out that in the real-world, database administrators do not use this traditional wisdom at all. So, I’ve been working on figuring out why they don’t use the traditional wisdom and what to do instead.

So those are the three things I’m pretty much working on.

AIM: What trends do you foresee in analytics, data and related technology in the coming future?

MS: Analysts have an insatiable desire to correlate more and more data. For example – several years ago, I had to make a sales call on Miller Brewing Company, and they have a traditional data warehouse for sales of beer by brand, zip code, etc.., which they used to forecast sales.  The year I visited them was a year that El Nino, which is a mid-equatorial upwelling of warm water, was predicted to be especially strong. It’s well understood that in El Nino years, it’s warmer than normal in the Northeast and it’s wetter than normal on the Pacific coast of the United States. So, I asked the Miller Beer guys that with the El Nino winter, if there is any correlation between beer sales and temperature or precipitation?” And they said that they would really like to know the answer to that question because, of course, it would impact beer sales in the coming winter. But weather data was not in the warehouse so they couldn’t ask that question.

So, business analysts just have an insatiable desire to correlate more and more features that could lead to better predictions. And I think that trend will continue and accelerate.

The second trend I expect is that predictive modeling is going to be applied to more and more application areas. Again, for example, I listened to a talk by a startup that was trying to predict what you ought to charge for hotel rooms in major cities. They assembled all kinds of data including how many people landed at the airport, etc… They predicted occupancy based on a whole bunch of these features and then it was a simple matter to build a pricing model that could change prices dynamically. It would never have occurred to me to say, “let’s apply predictive modeling to hotel occupancy or hotel pricing.” I think analytics and predictive modeling is just going to be applied more and more broadly.

The last thing – I think business intelligence will give way to data science in the data warehouse space. Business intelligence folks are very good at running front ends that issue SQL queries to data warehouses. For example, there were four hurricanes in Florida in the 2007 hurricane season and someone had to stock the Walmart stores during the hurricane season. So, a business intelligence person would find out what sold in the week before the hurricane, what sold in the week after the hurricane and compare that with same store sales in Georgia, produce a big chart of numbers and then plot all kinds of pictures. That’s pejoratively what a business intelligence person does. They’re basically SQL jockeys who produce charts and pictures of trends. On the other hand, if you’re a data scientist, you don’t look at hurricanes that way at all. You attempt to build a predictive model to predict what will sell based on a bunch of factors including the strength of the hurricane, etc.

As a business owner, would you rather have a big table of numbers or a predictive model? Everybody will say, “I’ll take the predictive model, thank you very much.”

AIM: Being a pioneer in database technology, what suggestions do you have for students looking to pursue this field and the startups keen on exploring it as a business opportunity?

MS: If you’re in school, get a computer science degree from the best computer science institution that you can get into. Then make sure you understand both database management and data science and become very adept at writing computer programs. Get adept at coding and learn about data management and data science.

As far as what startups to join, in the US, the market is awash in venture capital money for startups. Start with a good idea – one that at least a couple of enterprises are willing to buy. So basically, prove that there is a market for your idea. And secondly, prototype it to an extent that will demonstrate that it works. Prove there’s a market and prove you can build it, and based on that, you can probably get funding for your idea.

In terms of joining other people’s startups, I have basically the same advice. Make sure what’s being proposed is feasible and make sure there’s a market for it.

The post Interview with Michael Stonebraker: Distinguished Scientist and recipient of 2014 ACM Turing Award appeared first on Analytics India Magazine.

Demand for artificial intelligence and machine learning specialists in the country are expected to see a 60 per cent rise by 2018 due to increasing adoption of automation. Although AI and machine adoption is on the rise in India, there is negligible talent with experience in technologies like deep learning and neutral networks. As there is significant talent crunch in the AI space and AI roles are evolving into broader and more strategic productivity management roles, the demand for AI experts will only increase in the future in India.

Program Objective:

• Understanding of Decision Science Course and its application in today’s world
• Demand and rise of AI Experts
• Industry examples to better understand this – Case Study Followed by Question and Answer Session

Webinar Session by: Dr. Shailesh Kumar

Date: 14th September, 2017

Time: 4:30pm – 5:30pm

Dr. Kumar’s Profile:

Currently:

• Distinguished Scientist and Vice President at Ola Cabs
• Visiting Faculty of Machine Learning at Indian School of Business

Previously:

• Chief Scientist and Co-Founder, ThirdLeap – an AI EdTech company.
• Machine Learning Researcher at Google, Bing, Yahoo!, and Fair Isaac.

A TEDx speaker, Dr. Kumar has been an invited and keynote speaker at a number of public and corporate data science events including the Fifth Elephant, Data Science Congress, and NASSCOM Big Data & Analytics Summit. He serves as a Data Science advisor to a number of Startups, Tech companies, VC’s, and government agencies.

Dr. Kumar was recognised as one of the top ten data scientists in India in 2015 by the Analytics India Magazine. He holds twenty patents and has published twenty international publications and book chapters in these areas.

Dr. Kumar received his PhD in Electrical and Computer Engineering and Masters in Computer Science, both from the University of Texas at Austin, USA. He received his B-Tech. in Computer Science and Engineering from the Institute of Technology.

Register for the Webinar

The post Webinar: The rising demand for AI experts | Sep 14, 2017 4:30 PM – 5:30 PM appeared first on Analytics India Magazine.

With automation throwing traditional businesses out of balance, and the increasing adoption of data science for business decision making in every sector, analytics has become a highly sought-after skill for mid-career professionals. Executive education in analytics has never been more important than now. Explore your career options in analytics and learn how the Executive Program in Business Analytics by Jigsaw Academy and MISB Bocconi can help you be a step ahead of the competition.

Speaker Name: Gaurav Vohra

Designation: Founder & CEO, Jigsaw Academy

Bio: Gaurav Vohra was voted as one of the Top 10 Prominent Analytics Academicians in India in 2015 and 2017. He has over 15 years of experience in the field of analytics and has worked across multiple verticals including financial services, retail, FMCG, telecom, pharmaceuticals, and leisure industries.

Gaurav holds an MBA from the prestigious IIM Bangalore, and started his career in analytics with Capital One and over the last decade has worked with clients such as JC Penney, Gap, Walmart, IRI, 7-11, IMS health, and ICICI Lombard. With his firm belief in the power of analytics for business growth, Gaurav founded Jigsaw Academy in 2011 as an avenue to meet the growing demand for talent in the field of analytics by providing industry-relevant training and education to develop business-ready professionals.

Day/ Time: Tue, Sep 26, 2017 3:00 PM – 4:30 PM IST

Register Here

The post Webinar: Beat Mid-Career Blues with Analytics Upskilling | Tue, Sep 26, 2017 3:00 PM IST appeared first on Analytics India Magazine.

Ravi Gupta from MicroFocus India will be sharing Advance Analytical capability of Vertica Platform and will focus on Prediction and Modelling capability inbuilt in Vertica Platform. He will share different predictive options available in the platform and will share a running demo use case about Vertica Predictive Maintenance capability that how a Telecom company uses such Solution to predict the Tower failure possibility and take corrective action in advance.

Speaker: Ravi Gupta

Designation: Vertica Presales Head, India and SAARC Region, MicroFocus India

Ravi is Vertica Presales Head for India and SAARC Region. He has around 15 years of IT experience and out of which half of his career is with Vertica and Big Data Technologies. He has worked with World Wide customers at different geographies. Ravi has excellent Vertica Product knowledge and its feature and how Vertica Technology has helped Customers in their respective Verticals. Ravi has worked extensively with APJ Region Customer in the field of Banking, Telecom & Retail.

Day/time: Wed, Sep 27, 2017 3:00 PM – 4:30 PM IST

Register here.

The post Webinar: Predictive Analytics with Vertica | Sep 27, 2017 3:00 PM appeared first on Analytics India Magazine.

Courtesy: Sandeep Mittal

The post Toon: Cypher 2050 appeared first on Analytics India Magazine.

We receive plenty of queries from senior professionals with upwards of 10 years of work experience who are considering a career change into the field of Analytics and Big Data. Now after spending a considerable amount of time and effort in building a career in a specific domain, it is but natural for them to have numerous questions raging in their heads about making that jump.

Will I lose out on all my previous experience?

Will I have to start from scratch?

What kind of skills should I pick up?

Do I need to learn a programming language?

……to name a few.

In a tête-à-tête with Asis Mohanty, a senior professional at DBS Bank with over 12 years of experience, he recounts his experience during the 10-month intensive Executive Program in Business Analytics by SDA Bocconi and Jigsaw Academy. He talks to us about his learning as well as tips for people looking to go down this path.

Let’s hear it from Asis himself.

Analytics India Magazine: Why and how did you choose to do this course?

Asis Mohanty: Anyone who spent time in the banking industry understands the power of data and also that analytics is the skill of the future. I had been playing around with the idea of doing a long-term analytics course for some time now. However, I wanted to make sure I enroll for a course that gives me a good flavor of both Analytics and Big data. After a good amount of research on my part, I narrowed down to the Executive Program in Business Analytics (EPBA) offered by SDA Bocconi and Jigsaw academy because I found the curriculum to be most comprehensive and I felt that the learning methodology of the course was designed to maximize business exposure.

AIM: If you had to summarize your overall experience with this course….

AM: After completing the course, I can say that the course lived up to my expectations. I felt like a student again after a long time. I think the best part of the course was the fact that it was not easy, it pushed me to the core and that is what I would expect from a Master’s program.

AIM: Was your company supportive of your decision and has it benefitted them in any way? 

AM: My management was very supportive about this decision as they are keen on building strong analytics competency within the team. In fact, we are all encouraged to take on self-study initiatives, upskilling via online and executive programs and similar initiatives are encouraged within the company. My course was partially sponsored by the company and in exchange I have taken up the role of evangelizing analytics within my organization. I have conducted several classes for my team and plan to do more in the future. I can see many areas and roles which would benefit from analytics and I am doing my best to make the organization more data-driven.

AIM: How was the course curriculum?

AM: As I said, one of the reasons I chose this course was because it offers the right mix of Analytics and Big Data with a strong focus on business application. I feel the course does a great job of linking theoretical concepts with practical application. Given the direction the industry is headed, I would like to see content on machine learning and cloud added to the curriculum.

AIM: What about the faculty?

AM: We had an interesting mix of faculty, with professors from Italy and industry experts from Jigsaw Academy. The SDA professors are very good and their teaching is very industry focused. In terms of industry experts, we had someone from Vodafone, someone from the banking domain, Big Data architect and several others. All these sessions were very informative.

AIM: What about the Capstone project?

AM: I think the capstone project is a very important part of the learning process and the course puts a strong emphasis on it. If you take the project seriously, it can be the best source of learning. I worked on a very interesting project where we had to optimize the system so we could offer the right product to the right customer at the right time. It was a great opportunity for me to brush on my logistic regression skills and pick up additional machine learning skills with real world data.

AIM: Finally, any tips for aspiring data scientists? Especially those with 10+ years of experience?

AM: Firstly, let me clarify. My current role is not an out and out Data Scientist role. But I do work a lot with data. I feel I am a lot more confident making decisions backed by data now. My advice to people would be:

• Get involved in the capstone project right from the start. It’s a great learning opportunity if you are serious.
• Write blogs. Be active on Linked In groups and other Data Science communities. Build your profile. Don’t wait till the end of the course for this. Start early. You will not regret it.
• Analytics projects are very iterative. You will try many things that wont work till something suddenly clicks. So don’t give up. Keep trying.
• For senior folks, always keep an eye on the bigger picture. Keep abreast of the tech landscape. Know what is hot and what is not. Attend forums and summits to keep your knowledge up to date.
• Again, for senior guys, I want to remind them the pace with which the world is changing has increased tremendously. You have to keep learning new things, new skills. That’s the only way to stay relevant.

And lastly, don’t shy away from becoming a student again. Believe me, you will love the experience.

So yes, banking on big data pays off big for senior professionals.

The post Is Banking on Big Data the Right Move for Senior Professionals? appeared first on Analytics India Magazine.

When we think about Data Science and Data Scientists, we sort of see a whole series of news flashes about layoffs in the IT sector, artificial intelligence and self-driving cars in our heads.

But despite all of that, in this era of Big Data, number crunching and analytical thinking, there is a definite and irrevocable place for one key human emotion in the modern workplace — the intuition.

This is what the protagonist of Analytics India Magazine’s upcoming web series ‘The Dating Scientist’, to be released on 9 October 2017, is set to tackle.

Vishesh, the hero of the three-part web series is a corporate employee, with a thorough base in marketing. But his unique marketing brain is on the verge of becoming useless, thanks to the newly emerging field of Data Science. Such is the aura of data science among urban employees nowadays, that each of them is thinking in his head – “May be data science is the new sexy”, and Vishesh is no exception. ‘The Dating Scientist’ explores this transition to the new era of data and its effects on job profiles.

When Vishesh finds out that his organisation is also going the tech way by hiring a new Data Scientists, the marketing pro is first antagonistic towards his colleague. Of course, his antagonism gives way to different emotions altogether when he realises that his new Data Scientist colleague and roommate is a painfully shy girl named Manya.

Bhasker Gupta, founder and CEO, Analytics India Magazine said: “Professionals often think of Data Scientists as these alien creatures who only exist to snatch their jobs away. With ‘The Dating Scientist’ we want to bust that perception and showcase that not only Data Scientists are becoming integral part of the work environment, but also that they are very much prone to the same challenges and stressful situations like everyone else.”

In a series of hilarious twists, Vishesh, whose job is at stake, and Manya whose lack of social personal skills may cost her dearly, reach an impasse, where only a bet can save them both.

Set in the city of Bengaluru, ‘The Dating Scientist’ has been directed by Hera Pheri Films, a venture by a group of young engineers from IIT Kanpur.

Episode 1

The post Is Data Science The New Sexy? Analytics India Magazine’s New Web Series ‘The Dating Scientist’ Tackles The Question appeared first on Analytics India Magazine.

Since the first experiments of Electroencephalography (EEG) on humans by Hans Berger in 1929, the idea that brain activity could be used as a communication channel rapidly emerged. EEG is a technique which measures, on the scalp and in real-time, small electrical currents that reflect brain activity. As such, EEG discovery has enabled researchers to measure brain activity in humans and to start trying to decode this activity.

Read the complete article here.

The post Infographic: 10 Real Life Examples Of BCI Devices That You Can Control With Your Thoughts appeared first on Analytics India Magazine.

Artificial Intelligence is no more a buzzword. It has grown over the years to be the most disruptive technological trend in recent years, with tech behemoths racing to prove their supremacy in this space. Its evident, that the future race for tech is dependent on pretty much who takes over this industry.

Across all the frenzy that we are seeing around AI, where does India really stand in this space? Tech outsourcing has always been a strong forte of India, especially in the space of enterprise technology servicing. In this independent study, we try to bring in some metrics around AI industry in India. We look at AI in India as an industry from a very bird’s eye view perspective – without dwelling into the nitty gritty of how it is shaping up but more on what the numbers are telling us.

Also, an important piece to keep in mind while reading these numbers is that AI has a lot of overlap with Analytics/ Data Science industry; atleast the underlying algorithms and methodologies. So, the numbers might also reflect some biases towards analytics industry as well as rebranding of a lot of data science work into being called as AI. Yet, the whole idea to do this study, is to put some sense into the current chaos around AI.

Key Trends-

• Artificial Intelligence Industry in India is currently estimated to be $180 Million annually in revenues.
• There are approximately 29 Thousand AI professionals in India.

AI Professionals in India-

• The average work experience of AI professionals in India is 6.6 years.
• Around 2,400 fresher were added to AI workforce in India this year.
• Almost 55% AI professionals in India have a work experience less than 5 years.
• Top universities/ schools that AI professionals in India graduate from are

University of Mumbai
BITS, Pilani
IIT, Kharagpur
University of Pune
IIT, Delhi
IIT, Bombay
IIT, Kanpur
Delhi University (321)
IIT, Roorkee

• Almost 38% of AI professionals in India are employed with large sized companies – with more than 10K total employee base.
• Mid size organizations (total employee base in range of 200-10K) employ 30% of all AI professionals in India.
• Startups (less than 200 employee base) employees 33% of AI professionals in India.

AI Companies in India

• More than 800 companies in India claim to work on AI in some form. This includes a small number of companies into products and a larger chunk offering either offshore, recruitment and training services.
• Moreover, the number of AI companies in India are still very few in number, compared to the strength of AI companies around the globe. In fact, India accounts for just 6% of global AI companies.
• On an average, Indian AI companies have 188 employees on their payroll.
• Almost 84% of AI companies in India have less than 50 employees.

AI Salaries in India

• The average salary in AI Professionals in India for year 2017 is INR 14 Lacs across all experience level and skill sets.
• On average, AI professionals receive around 20% higher salaries than Analytics/ Data Science professionals in India.
• Almost 42% of AI professionals have salaries under 6Lakhs.
• 27% AI professionals in India earn in the salary bracket of 10-25L and almost 5% AI professionals earn more than 50L.

AI Jobs in India

• While, it is difficult to ascertain the exact number of AI jobs openings; by our estimates, close to 4,000 positions related to AI are currently available to be filled in India.
• The top 5 skillsets that AI Employers are looking for are Machine Learning, Natural Language Processing, Neural Networks, Analytics, Pattern Recognition.
• Compared to worldwide estimates, India contributes just 7% of open jobs opening currently. The no. of jobs in India are likely to increase much faster vs. the rest of the world as more AI related projects get outsourced to India due to lack of skills across the world.
• 10 leading organizations with the most number of AI opening this year are – Accenture, Wipro, Adobe, JPMorgan, Accenture, Amazon, SAP, L&T Infotech, Nvidia & Intel.
• In terms of cities, Bengaluru accounts for around 37% of AI jobs in India.
• Delhi/ NCR comes second contributing 23% AI jobs in India and approximately 13% of AI jobs are from Mumbai.

• Banking & Financial sector is the biggest influencer in AI job market. 47% of all jobs posted on AI were from the banking sector.
• E-commerce account for 21% of AI jobs.

The post Study – State of Artificial Intelligence in India 2017 appeared first on Analytics India Magazine.

Podcasts have emerged as an important medium to share information through. Though not as popular as the video, there are tons of very interesting podcasts that are being produced regularly.

So, we decided to hand-curate a list of interesting and popular podcasts around the topic of AI and machine learning. Do give a listen!

Talking Machines

By Tote Bag Productions

Talking Machines is your window into the world of machine learning. Your hosts, Katherine Gorman and Neil Lawrence, bring you clear conversations with experts in the field, insightful discussions of industry news, and useful answers to your questions. Machine learning is changing the questions we can ask of the world around us, here we explore how to ask the best questions and what to do with the answers.

Data Skeptic

By Kyle Polich

The Data Skeptic Podcast features interviews and discussion of topics related to data science, statistics, machine learning, artificial intelligence and the like, all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches.

Artificial Intelligence

By Patrick Winston, Mark Seifter

In these lectures, Prof. Patrick Winston introduces the 6.034 material from a conceptual, big-picture perspective. Topics include reasoning, search, constraints, learning, representations, architectures, and probabilistic inference. In these mega-recitations, teaching assistant Mark Seifter works through problems from previous exams in a lecture-style setting. Students are asked to participate, and emphasis is placed on being able to work the algorithms by hand.

This Week in Machine Learning & AI Podcast

By Sam Charrington

This Week in Machine Learning & AI brings you the week’s most interesting and important stories from the worlds of machine learning and artificial intelligence. We discuss the latest developments in research, technology, and business, and explore interesting projects from across the web. Technologies covered include: machine learning, artificial intelligence, deep learning, natural language processing, neural networks, analytics, big data and more.

Learning Machines 101

By Richard M. Golden, Ph.D., M.S.E.E., B.S.E.E.

Smart machines based upon the principles of artificial intelligence and machine learning are now prevalent in our everyday life. For example, artificially intelligent systems recognize our voices, sort our pictures, make purchasing suggestions, and can automatically fly planes and drive cars. In this podcast series, we examine such questions such as: How do these devices work? Where do they come from? And how can we make them even smarter and more human-like? These are the questions that will be addressed in this podcast series!

Machine Learning Guide

By OCDevel

This series aims to teach you the high level fundamentals of machine learning from A to Z. I’ll teach you the basic intuition, algorithms, and math. We’ll discuss languages and frameworks, deep learning, and more. Audio may be an inferior medium to task; but with all our exercise, commute, and chores hours of the day, not having an audio supplementary education would be a missed opportunity. And where your other resources will provide you the machine learning trees, I’ll provide the forest. Additionally, consider me your syllabus. At the end of every episode I’ll provide the best-of-the-best resources curated from around the web for you to learn each episode’s details.

Linear Digressions

By Ben Jaffe and Katie Malone

In each episode, your hosts explore machine learning and data science through interesting (and often very unusual) applications.

The AI Podcast

By NVIDIA

AI has been described as “Thor’s Hammer“ and “the new electricity.” But it’s also a bit of a mystery – even to those who know it best. We’ll connect with some of the world’s leading AI experts to explain how it works, how it’s evolving, and how it intersects with every facet of human endeavor. This podcast is produced by NVIDIA, the AI computing company. Multiple episodes are released every month.

Artificial Intelligence in Industry with Dan Faggella

By Dan Faggella, Founder of TechEmergence

Artificial intelligence is more interesting when it comes from the source. Each week, Dan Faggella interviews top AI and machine learning executives, investors and researchers from companies like Facebook, eBay, Google DeepMind and more – with one single focus: Gaining insight on the applications and implications of AI in industry. Follow our Silicon Valley adventures and hear straight from AI’s best and brightest.

Partially Derivative

By Partially Derivative

The everyday data of the world around us, hosted by data science super geeks. For the nerdy and nerd curious.

The post Top 10 Podcasts on Machine Learning & AI that you must follow appeared first on Analytics India Magazine.

Analytics industry in gaining importance in India and is being deployed across various sectors such as banking, finance, e-commerce, retail, and telecom. Tapping on to the growing analytics industry, the study gives us a quick insight into how the analytics scenario is evolving in the domestic market.

This year’s study has been co-presented by Cartesian Consulting, a global analytics services firm specialising in customer, marketing, and business analytics. We looked at 20 large Indian firms across industries that have adopted analytics to improve  business.

Read: Study – State Of Analytics In Domestic Firms In India 2016

Types of Analytics firm in India

There are three major players — service providers (who provide analytics services to the end  consumer), captive centres (back offices that do outsourced work) and the domestic market. Service providers have been dominating the analytics market (their share of the market is ~75%) for quite some years and it is only now that the other two players are becoming equally competent. In fact, the domestic market accounts for just 5% of the Indian analytics market. Armed with their own analytics team, these firms use analytics for their internal processes.

Key findings

• Like last year, it is the private banks such as Kotak Mahindra Bank, Axis Bank, ICICI, HDFC, Yes Bank that are deploying analytics extensively. Among the public banks, SBI is the most analytics savvy. In the insurance space, Max Life Insurance leads the pack.
• Among the ecommerce players, the frontrunners are Flipkart and Ola, while Patym and Zomato lag substantially.
• In telecom, Idea Cellular has embraced analytics faster than big players like Bharti Airtel.
• In the automobile sector, Mahindra Group and TATA Motors are upping the game.
• Considering the relative focus on advanced analytics by other conglomerates, Aditya Birla Group, Reliance Industries, L&T, ITC have a decent focus on analytics.

Analytics at Indian firms – Key trends

• 44% of all analytic functions for Indian firms are based out of Mumbai.
• Almost 85% of analytics functions are based out of just three cities – Mumbai, Bangalore & Delhi/NCR.
• The top 10 schools from which Indian firms recruit their analytics teams are: University of Mumbai, Delhi University, NMIMS, IIM Calcutta, The Institute of Chartered Accountants of India, IIM Lucknow, IIM Bangalore, IIM Ahmedabad & ISB  Hyderabad.

Indian firms- Key Metrics for Analytics Function

• On an average, Indian firms have an analytics penetration of 1.7%. This essentially implies that for every 59 employees in the organisation, one employee is associated with data and analytics.
• Analytics penetration is typically higher in new age ecommerce firms and lower in traditional businesses.
• The average tenure of analytics professionals in Indian firms is 3.4 years.
• TATA Motors boasts the highest tenure among  India firms, whereas new age ecommerce start-ups have a much lower analytics tenure.
• On the other hand, advanced analytics/ data      science as a percentage of total analytics function is higher in new age ecommerce firms in India and lower in traditional firms.
• On an average, analytics professionals in Indian firms have 8 years of work experience.
• Zomato has the lowest seniority in terms of analytics professionals in India firms.
• TATA Communications has the highest seniority.

Analytics Penetration vs. Maturity- Conclusion that can be made from Graph

• Like last year, there is a slight negative correlation between maturity and penetration.
• Flipkart is the only company in the first quadrant, which       implies it is both high on penetration and maturity. Given Flipkart was amongst the first companies in the ecommerce space to adopt analytics, it has ramped up its  practice both in terms of size and quality of work.
• Most companies seem to lie in quadrant III. This could be   attributed to how just few analytics companies have aggressively adopted analytics. Mostly, the new age ecommerce firms like Ola, Paytm & Zomato took a very aggressive stance on their analytics function last year, also coupled by large funding budget they had.
• Zomato tops in analytics penetration and is lowest in analytics maturity.
• Sitting at the bottom of quadrant III, SBI’s analytics penetration and maturity profile suggests the bank needs to push itself harder if it is to compete with private banks in ranking.
• TATA Communications has the highest analytics maturity of all domestic firms in India.
• Quadrant III has many banks – Kotak Mahindra, ICICI, Yes Bank, HDFC and Axis Bank. It would have been great to see them building their analytics capabilities quicker than their older peers in quadrant II.

Sector view – Analytics Penetration vs. Maturity

• While ecommerce firms have the highest analytics penetration of all sectors, they have the lowest maturity.
• Analytics build-up for ecommerce is so high that it makes other sectors appear in an almost vertical line (having the same penetration).
• Massive analytics build-up by mostly new age firms also means that the focus on maturity in terms of tenure, seniority and advanced analytics is yet to kick in.
• Automobile firms have the highest maturity and banking firms the lowest.

Banking–  Analytics penetration vs. maturity

• Max Life Insurance has emerged as one of the largest adopters of analytics since last year’s list.
• It ranks highest in terms of analytics maturity, higher than most private sector banks.
• Axis Bank has higher analytics maturity compared to ICICI, HDFC and Kotak Mahindra.
• SBI  has the lowest analytics maturity and analytics penetration.

Conglomerates–  Analytics penetration vs. maturity

• ITC has the highest analytics penetration of all  other companies in the domain, yet it has the lowest maturity.
• Reliance Industries leads in terms of analytics maturity whereas ITC is the lowest.
• Mahindra Group has a relatively higher analytics maturity than Aditya Birla Group but lower analytics penetration.
• In terms of focus on advanced analytics and data science, Reliance Industries is ahead of the pack.

E-commerce– Analytics penetration vs. maturity

• Ecommerce firms are biggest adopters of analytics and they are focussed on hiring senior analytics professionals.
• Flipkart is a leader in the ecommerce segment with a significantly high analytics maturity. It,   however, lacks in terms of analytics penetration, which is led by Zomato followed by Ola.
• Zomato, however, lacks in analytics maturity.
• The analytics build up is highest in Flipkart.

Telecom– Analytics penetration vs. maturity

• Both in terms of analytics maturity and penetration, TATA Communications is ahead of big players such as Airtel and Idea.
• This can be attributed to the fact that the company boasts the highest number of senior analytics professionals and also to their focus on advanced analytics and data science.
• Idea has the lowest analytics maturity.

Here’s the complete Study

Download the complete Study:

The post Study: State Of Analytics In Domestic Firms In India 2017 – by AIM & Cartesian Consulting appeared first on Analytics India Magazine.

Artificial Intelligence is the most trending technology right now. According to our research, there are close to 4000 open job positions in India currently, and most of them were created in last 6 months.

Yet, in terms of education, this area is still in a very nascent stage. Here we list down 10 places from where professionals in India can take a formal education on AI.

Please note:

• This list is completely different from the analytics training institutes and courses ranking that we come out each year.
• Given the nascent stage of the industry, we have clubbed all formats courses in one list (i.e. long/ short format, online/classroom etc)
• This is not a ranking, it’s the list of 10 most relevant avenues we found in India that someone can utilize to learn AI
• There are many courses that focus on Machine learning. In this list, we included programs that are AI focus and not just ML.

PG Diploma in Machine Learning and AI – Upgrad

Format: Online

Duration: 11 Months

Fees: Rs. 2,75,000

This is an industry-relevant yet academically-rigorous 11 month program covering Machine Learning and AI concepts. It is specifically designed for working professionals with Math/Software Engineering/Statistics/Analytics backgrounds to help them gain practical knowledge and accelerate entry into Advanced Data Science and Machine Learning roles.

Foundations of Artificial Intelligence and Machine Learning – IIIT Hyderabad

Format: Weekend Contact Sessions

Duration: 15 Weeks

Fees: Rs. 2,75,000

The Machine Learning Lab acts as an umbrella organization at the institute to both strengthen the existing groups and facilitate new activities in related areas. It also acts as a force multiplier in attracting projects and funding from other entities in the government and industry sectors, coordinate research in related domains across different centers of IIIT Hyderabad, as well as in the institute’s research collaboration with other academic institutions in the country.

Master of Technology in Artificial Intelligence – University of Hyderabad

M.Tech Artificial Intelligence is also a four-semester course including two semesters of course work and two semesters of project work. This programme is meant for students already well equipped in computing sciences and as such imparts advanced training in all the major areas of artificial intelligence and other emerging technologies, such as machine learning, data mining, etc.

M.Tech. Computer Science Specialization In Artificial Intelligence – UPES

Tech. Computer Science & Engineering with Choice based specialization in Artificial Intelligence, Image Processing, Data Analytics and Security is a specialized program aimed at providing the student with in-depth knowledge of four key domains in Computer Science and Engineering. Artificial Intelligence, Image Processing, Data Analytics and Security and Forensic technologies has acquired widespread acceptance and adoption among enterprise business applications and research community. Several startups in recent times have endorsed the presence of these domains.

Artificial Intelligence & Machine Learning Training – Techtrunk

Format: Online Live Led Class

Duration: 45 hours

Fees: Rs. 18,500

If you are a computer geek, if you love coding and wish to explore dimensionless world of programming, TechTrunk brings you the core Artificial Intelligence Training which will take you through core development and programming experience and will make you expert in writing algorithms for AI applications, you will learn Machine Learning, Fuzzy Logic, NLP, SVM and much more.

Artificial Intelligence (AI) Training in Hyderabad – AnalyticPath

At the end of Artificial Intelligence Training Sessions in Hyderabad, aspirants can easily build great command over each and every module to face business world challenges in an optimized way.

Artificial Intelligence Nanodegree – Udacity

This Nanodegree program consists of two terms of three months each. Students must complete the full six months to earn their credential and graduate. Each term costs ₹44,900 (taxes extra), paid at the beginning of each term. At this time, there are no scholarships or financial aid available.

Artificial Intelligence Training – Zekelabs

This artificial intelligence course is for Python programmers looking to use artificial intelligence algorithms to create real-world applications. This artificial intelligence course is friendly to Python beginners, but familiarity with Python programming would certainly be helpful so you can play around with the code. It is also useful to experienced Python programmers who are looking to implement artificial intelligence techniques.

Machine Learning and Artificial Intelligence (AI) – myTectra

Format – Live Online & Classroom (Bengaluru)

myTectra offers Artificial intelligence training in Bangalore using Class Room. In this course you will learn to implement mathematical ideas in machine learning. You will investigate the process of learning and understand the application of various learning algorithms.

Artificial intelligence & Machine Learning Training in Bangalore – Zenrays

ZenRays offers the best Artificial Intelligence Training in Bangalore and Machine Learning Training in Bangalore. Register to attend our Hands-on Training. Work on Machine Learning Live Project in Bangalore as well as Artificial Intelligence Live Project. Classroom and Online Training facilities are available for all the sessions and technology training courses in Bangalore at ZenRays.

The post 10 Leading Courses and Training Programs on Artificial Intelligence in India appeared first on Analytics India Magazine.

It has been two years since Analytics India Magazine took up the initiative of identifying the brains behind the excellent work that is being done in the field of data science in India. Given a rise in popularity in the field, there are many professionals who are veering their way onto the data science field and India is witnessing a growing number of data scientists, playing crucial roles in various industries.

For this year’s ranking, data scientists from various organisations and those working independently, were considered, irrespective of size and nature of work. Like last year, we also got in touch with data scientists that we know personally who might not necessarily be associated with an organisation.

The top 10 names were concluded based on various parameters like pedigree, patents, papers and technical publications authored, competitions participated, pioneering work, knowledge and applicability of tools, ability to convince multiple stakeholders through data insights and many more. We also considered the inputs from expert team of leaders, past data scientists and evangelist, and their overall contribution to the analytics industry in the country, to file the list.

Here’s the list of top 10 data scientists, to draw some inspiration and motivation from (in alphabetical order).

Read our last year’s list here

Ankur Narang

Dr. Narang leads the Data Science & AI practice at Yatra Online Pvt. Ltd. as Senior Vice President, Technology & Decision Sciences. He has 23+ years of experience in Senior Technology Leadership positions across MNCs including IBM Research India and Sun Research Labs, CA, USA.

With a B.Tech. & PhD IIT Delhi in CS&E, he has 40+ publications in top international Computer Science & Machine Learning conferences and journals, along with 15 granted US patents. He has held multiple Industrial Track and Workshop Chair positions, and has given invited talks in multiple conferences.

As Senior Research Scientist at IBM Research, for key Telecom players in India and US, he designed and delivered real-time parallel recommendation algorithms, high throughput streaming analytics workflows and distributed graph analytics using GPUs over EDR/CDR data. Further, he led design & implementation of AI based cognitive workflows for inverse problems using oil & gas production data. As Chief Data Scientist and AVP Data Science at Mobileum, he led development of voice and data fraud techniques using deep learning and travel prediction and campaign models over terabytes of CDR data. As CTO in a recent startup stint, he developed game theory, ML and optimization based novel approaches to pricing and revenue management for large Media & FMCG companies. At Yatra, he is working on AI based approaches for marketing and discounting optimizations and personalized chatbot experience.

Avik Sarkar

He currently heads the Data Analytics Cell at NITI Aayog (National Institution for Transforming India) as Officer on Special Duty (OSD) and believes that the challenges with analytics in governance are quite different from that of other industries. He is in charge of developing roadmap for use of data/analytics for Governance and Policy making along with providing analytical insights for policy making across sectors like Direct Benefit Transfer, Innovation, Digital payments, Healthcare/Nutrition, Agriculture, etc. He is engaged with the energy vertical at NITI Aayog where he is instrumental in various long term planning of future energy needs of India through initiatives like integrated energy modelling, energy data management, etc.

With over 15 years of experience across different aspects of data analytics, statistical modeling, data and text mining across companies like IBM, Accenture, Nokia, NASA, Persistent Systems, etc., he has also contributed to various data and analytics related engagements with Singapore Government. While at IBM, Dr. Sarkar made significant contributions towards developing the Monte Carlo Simulation of SPSS and the Predictive Maintenance and Quality solution for the manufacturing sector.

He holds a PhD from The Open University, UK, Masters from Indian Institute of Technology (IIT) Bombay and Bachelors from Calcutta University and has authored several technical publications and technology patents.

Kiran R

Kiran R is currently the Director, Data Sciences at VMware and has an experience in driving impact in both B2B and B2C organizations. He currently drives advanced analytics and data sciences support at VMware across verticals.

Prior to VMware, he headed analytics & data sciences for Sales, Marketing & Customer at Flipkart, affiliates analytics at Amazon and for the e-business & search teams at Dell. Kiran has 3 filed US patents. During his days at Dell, he was awarded the 2012 India Innovator of the Year award by Michael Dell in person in 2012. He is the author of a Harvard Business Review Case study on data mining in partnership with IIM-B, that is taught in premier schools around the world.

Kiran is a Kaggle grandmaster and was ranked in the top 10 Kaggle data scientists in 2013-14 with a top rank of 7, which remains the highest ever rank consistently held over a year by an Indian to this date. He is one of the winners of the prestigious KDD Data Mining Cup in 2014, organized by ACM-KDD (Association for Computing Machinery – Knowledge, Discovery and Data Mining).

A computer science engineer and a post-graduate from Indian Institute of Management Kozhikode (IIM-K), Kiran is passionate about data sciences at work and outside of it.

Nitin Sareen

Nitin is a strong believer and practitioner of using advanced analytics as a strategic differentiator across domains and has a proven track record of creating impact by solving key business problems with astute analytical problem solving.

He currently leads the Data Science group at WalmartLabs to leverage big data, data science & technology to enable faster & smarter business decisions. He is leading key initiatives to deploy algorithmic products that consume Walmart scale data and infuse smarter decisions across retail lifecycle like site selection, assortment optimization, pricing, demand forecasting, supply chain, store ops and enterprise wide decisions. These solutions are able to deliver multi-billion $ impact. The group is driving innovation leveraging emerging technology to experiment with AI; deep learning driven solutions for image, video & text analytics across various use cases. He is considered an analytics thought leader who is enabling creation of value for the organization.

Prior to this he has held various key positions in MNCs like Citigroup, HSBC, FICO and GE across multiple roles. He was responsible for setting up and managing analytics groups in the areas of insurance analytics, consumer finance and retail credit risk management and gathered functional expertise across marketing and risk analytics. He has been focused on continuous learning and professional development for self and the teams he has led.

An alumnus from Indian Statistical Institute (ISI), Calcutta, Nitin has over 17 years of extensive experience in the field of predictive analytics and data science projects. He is also an active speaker and panelist at leading data science conferences.

Om Deshmukh

Dr. Om Deshmukh is the Director, Data Sciences in the Analytics Centre of Excellence at Envestnet | Yodlee. Om and team drive foundational data sciences initiatives to mine actionable intelligence from the petabytes of data that flows through the Envestnet | Yodlee platform, enabling financial service providers to improve consumers’ financial wellness.

He received his PhD from the Department of Electrical and Computer Engineering at University of Maryland, College Park, MS from Boston University and B.Tech. from BITS-Pilani.

Prior to Envestnet | Yodlee, Om has worked at IBM Research and Xerox Research and has a deep expertise in machine learning and data analytics, particularly Bayesian Non Parametrics and Generative Models using Deep Neural Networks. At Xerox Research, he built a team of highly motivated researchers and developers who designed state-of-the-art Machine Learning and Multimedia (speech/text/video) Data Analytics algorithms. He successfully negotiated a first of its kind revenue sharing deal with an edtech startup to inject multimedia analytics into its products.  At IBM, he built consensus among executives and presented to the CEO, a technology strategy for an analytics-driven billion-dollar business in ‘Personalized Learning.’

Om has filed 45+ patents (including 2 high-value patents) and was published in 50+ international publications (multiple best paper awards).

Ramasubramanian (Ramsu) Sundararajan

Ramasubramanian (Ramsu) Sundararajan heads the AI function at Cartesian Consulting. He started his journey as a researcher at the Indian Institute of Management in Kolkata, where he wrote his doctoral dissertation on theoretical and algorithmic aspects of learning with a reject option. Prior to this, he earned his undergraduate degree from the Birla Institute of Technology & Science, Pilani.

Since 2003, he has worked at GE’s Global Research division as well as at Sabre Airline Solutions’ Operations Research group, where he built statistical and machine learning models to find patterns in a variety of data sources in different domains. Examples include: patterns in the behaviour of retail banking customers that would indicate their future profitability or risk; patterns in medical image data that would provide early warning for diseases such as breast cancer and pneumoconiosis; patterns in sensor data that would help service engineers flag underperformance or impending failures in steam and gas turbines; and patterns in traveller behaviour that would help airlines determine trip purpose, ancillary purchase behaviour and so on. In his current role at Cartesian, he hopes to extend his hunt for interesting and actionable patterns to newer domains.

Through it all, Ramsu has maintained a focus on both business value and innovation. He has co-authored over 20 publications in international conferences and journals, notably Interfaces, Journal of the Operational Research Society and the Journal of Revenue and Pricing Management. A firm believer in the “if you want to learn, teach” doctrine, he has regularly offered internal training programs and delivered guest lectures at premier educational institutes such as the IIMs and IITs on applied machine learning.

Saigeetha A J

Saigeetha A J is a senior data scientist specialized in Computer Vision, Machine Learning, Geophysical modeling and Robotics, and currently leads a team of image/NLP experts at IBM. At IBM, she is extensively involved in delivering high end applications in image processing, video processing, OCR & Robotics along with NLP. Some of the significant contributions are quantifying wine brand popularity from social media images, video chunking & tagging, document digitization, Robotic presentation and path planning.

She has over 10 years of experience in various industries namely automotive, manufacturing, winery, education/learning, paints/chemicals, social media and disaster/hazard management. She holds a Ph.D in Physics from Gandhigram Rural Institute and carried out her research work in CSIR Fourth Paradigm Institute (formerly CSIR Centre for Mathematical Modeling and Computer Simulation). Her research work included development of a geophysical model for quantifying significant fault systems in India using GPS measurements.

She then later went on to work in a leading paint company in colorimetric data analytics including developing an empirical model to predict paint toner constituents in a paint formula from reflectance. After this, she led a team of image scientists in developing advanced computer vision algorithms for ADAS & driverless cars such as pedestrian & car detection, traffic light detection etc. at a leading multinational.

As a researcher, she has co-authored several research papers in leading journals of Elsevier, Indian Academy of Sciences. Apart from this, a couple of patents have been filed by her. Recently, she was adjudged “2017 Eminence & Excellence Star” performer at IBM.

Shantanu Bose

Shantanu has about 10 years of experience in devising data driven strategies for marketing and sales across various industries including Life Sciences, TMT and Retail. A post-graduate in Statistics & Informatics from Indian Institute of Technology, Kharagpur, he has extensive knowledge in complex statistical and machine learning models. His master’s thesis in image recognition was selected for presentation in 2008 IEEE Artificial Intelligence and Pattern Recognition Conference held in Florida.

He joined Deloitte from campus and was instrumental in developing a few solutions which defined the industry standard- gamified business simulation for a Retail client, disease propensity models using lifestyle data; the latter was featured in the front page in the Wall Street Journal as industry disruptor. He then joined Novartis, where he led the efforts in solving the problem of integrating planning and evaluation the online and offline promotions for pharmaceutical industry. He Joined back Deloitte 3 years back to start the Life Sciences advanced analytics offering where he has worked with most of the big pharma companies on various consulting engagements such as drug launch strategy, patient experience redesign and market map for effective product strategy. He has lent is rich experience with marketing and sales organizations in cross-industry engagements, he is currently leading the global sales strategy transformation project for a TMT client.

He is also engaged with the Deloitte product and innovation group to enhance and encapsulate some of the work on customer/consumer engagement and sales transformation to build solutions which can be deployed at scale across industries to shape product and marketing strategy – faster, smarter and in a cost effective way.

He has spoken in some of the leading data science conferences in India and abroad including Deloitte Analytics Summit and Cypher 2017.

Shankar Viswanathan

As part of ZS’s data science leadership team, Shankar is focused on shaping the advanced analytics capabilities of the firm to deliver real-world client impact powered by diverse data, algorithms and platforms. For over 16 years at ZS, Shankar has supported more than 20 global life science firms by providing fact-based insights for enhanced decision-making across a range of enterprise functions including sales, marketing and Health Economics and Outcomes Research (HEOR).

For the past eight years at ZS’s Capability and Expertise Center in India, Shankar has been instrumental in driving the analytical offerings spanning foundational performance engine analytics to next-gen analytics powered by unstructured data, predictive analytics, machine learning and AI. Prior to his current role, he was head of the Pune office and also led the business consulting capability for India.

Shankar has an interdisciplinary Ph.D. from Purdue (Artificial Intelligence + Operational Research applied to Chemical Engineering) and a B.Tech from IIT Madras. During the course of his doctoral thesis, he published 11 research papers on discrete event system representations to model batch process systems. He has been published in Value in Health, Society of Hospital Medicine and International Society for Pharmaceutical Outcomes Research (ISPOR).

Subramanian M S (Mani)

He heads the analytics at bigbasket.com – India’s largest online supermarket, with a focus on delivering definitive actionable insights that help enhance the customer experience. The analytics team at bigbasket leverages advanced tools, techniques and platforms to power a better customer experience. The analytics team under his guidance drives a.) diagnostic analytics to root-cause business problems, b.) predictive solutions including forecasting for perishable products, sales projections to support organization’s expansion and c.) prescriptive solutions including smart basket (a shopping assistant) and recommendations to help improve customer experience.

With an experience of more than 20 years in analytics leadership, he has worked with companies like Dell, McKinsey, Infosys, Ernst & Young and PwC. An undergraduate with engineering degree in computer science from the University of Madras, an MBA from IIM-Ahmedabad and with a graduate engineering degree in supply chain management from MIT, Mani is a frequent speaker in industry and academic forums as an analytics expert. Some of his experience include being a speaker at IIM-B’s Retail Master Class, panel moderator/member in various industry forums (Cypher, SCPC), conducting hands-on workshop in Analytics at UpGrad and other forums. He has also been part-time faculty delivering graduate level Analytics courses at SIBM, Bangalore and NMIMS, Bangalore.

The post Top 10 Data Scientists in India – 2017 appeared first on Analytics India Magazine.

ML Frameworks have become a standard paradigm these days. They not only democratize the development of machine learning algorithms but also speedup the process. And not just open source community, but large organization are also realizing the need to launch their own frameworks. Infact, just a month back Amazon’s AWS and Microsoft have together announced Gluon, a new open source deep learning interface.

We have sorted the machine learning frameworks on the basis of their popularity on web searches. Higher popularity indicates constant development, support and community engagement, which is needed requirement for experts using these frameworks. But first, let’s define it.

What is Machine Learning Framework

A Machine Learning Framework is an interface, library or tool which allows developers to more easily and quickly build machine learning models, without getting into the nitty-gritty of the underlying algorithms.

It provides a clear, concise way for defining machine learning models using a collection of pre-built, optimized components. Some of the key features of good ML framework are:

1. Optimized for performance
2. Developer friendly. The framework utilizes traditional ways of building models.
3. Is easy to understand and code on
4. Is not completely a black box
5. Provide parallelization to distribute the computation process

Overall, an efficient ML Framework reduces the complexity of machine learning, making it accessible to more developers.

10 Popular Machine Learning Frameworks

1.      Amazon Machine Learning

Amazon Machine Learning (Amazon ML) is a robust, cloud-based service that makes it easy for developers of all skill levels to use machine learning technology. Amazon ML provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology. Once your models are ready, Amazon ML makes it easy to obtain predictions for your application using simple APIs, without having to implement custom prediction generation code, or manage any infrastructure.

2.     Apache Mahout

Apache Mahout is a project of the Apache Software Foundation to produce free implementations of distributed or otherwise scalable machine learning algorithms focused primarily in the areas of collaborative filtering, clustering and classification. Many of the implementations use the Apache Hadoop platform. Mahout also provides Java libraries for common maths operations (focused on linear algebra and statistics) and primitive Java collections. Mahout is a work in progress; the number of implemented algorithms has grown quickly, but various algorithms are still missing.

3.     Apache MXNet

MXNet is a modern open-source deep learning framework used to train, and deploy deep neural networks. It is scalable, allowing for fast model training, and supports a flexible programming model and multiple languages. The MXNet library is portable and can scale to multiple GPUs and multiple machines. MXNet is supported by major Public Cloud providers including AWS and Azure Amazon has chosen MXNet as its deep learning framework of choice at AWS.

4.     Apache Singa

SINGA is an Apache Incubating project for developing an open source machine learning library. It provides a flexible architecture for scalable distributed training, is extensible to run over a wide range of hardware, and has a focus on health-care applications.

5.     Caffe2

Caffe2 aims to provide an easy and straightforward way for you to experiment with deep learning and leverage community contributions of new models and algorithms. You can bring your creations to scale using the power of GPUs in the cloud or to the masses on mobile with Caffe2’s cross-platform libraries.

6.     H2O

H2O.ai emerged in 2011 from a grassroots culture of data transformation. With H2O, the main product, a plethora of machine learning models (from linear models to tree-based ensemble methods to Deep Learning) can be trained from R, Python, Java, Scala, JSON, H2O’s Flow GUI, or the REST API, on laptops or servers running Windows, Mac or Linux, in the cloud or on premise, on clusters of up to hundreds of nodes, on top of Hadoop or with the Sparkling Water API for Apache Spark.

7.     Microsoft Cognitive Toolkit

The Microsoft Cognitive Toolkit—previously known as CNTK—empowers developers to harness the intelligence within massive datasets through deep learning by providing uncompromised scaling, speed, and accuracy with commercial-grade quality and compatibility with valrious programming languages and algorithms.

8.     Scikit-Learn

Scikit-learn (formerly scikits.learn) is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.

9.     TensorFlow

TensorFlow is an open-source software library for dataflow programming across a range of tasks. It is a symbolic math library, and also used for machine learning applications such as neural networks. It is used for both research and production at Google,‍often replacing its closed-source predecessor, DistBelief.

TensorFlow was developed by the Google Brain team for internal Google use. It was released under the Apache 2.0 open source license on November 9, 2015.

10.  Theano

Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation.

The post What is a Machine Learning Framework & 10 that you need to know appeared first on Analytics India Magazine.