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Which Data Science and ML platform is best for your business?

By Sachin Andhare

Automated feature engineering and AI-powered data preparation are the key differentiators for a code-free or code-first approach to data science Innovation, data, and analytics leaders looking for the best data science and machine learning platform have a hard nut to crack! Selecting a data science and machine learning (DSML) platform, given how fragmented the market is, where every vendor claims to be the ideal enterprise AI platform can be jarring. The challenge is even more complex for organizations that are new to machine learning or a traditional BI background without predictive analytics experience. And ditto for application developers and software architects searching for Cloud AI services to leverage AI and ML using APIs. What are some of the technical features that they need to consider? Which platform capabilities are most important? Gartner recently published the magic quadrant report for DSML platforms and evaluated over 20 platform vendors from AWS SageMaker,…

Does The Popularity of AutoML Mean the End of Data Scientist Era?

By Sachin Andhare

McKinsey Analytics wrote an article on the evolution of automated machine learning (AutoML) titled “Rethinking AI talent strategy as AutoML comes of Age.” McKinsey argues that the growing popularity of AutoML tools drives a radical new way of thinking about data science talent. By automating the data science process, AutoML platforms expand the reach of users to include business experts with extensive domain knowledge, non-data scientists, and operational experts. The key takeaway is that companies are best served by not putting all their resources into the fight for sparse technical data science talent but should instead focus at least part of their attention on building up their troop of AutoML practitioners, who will become a substantial proportion of the talent pool for the next decade. CIOs, data science, and analytics leaders will have to rethink their AI talent strategy fundamentally. The Covid-19 pandemic, budget cuts, and the pressure to do…

What Is Predictive Analytics and How To Get Started

By Sachin Andhare

TL: DR: Predictive Analytics is using historical and real-time data to generate useful insights and predicting critical outcomes in the future. A large number of organizations are leveraging this AI-powered technique to reduce risks, improve operations, cut business costs, and increase the bottom line. What is Predictive Analytics? Gartner defines Predictive Analytics (PA) as a form of advanced analytics which examines data or content to answer the question “What is going to happen?” or more precisely, “What is likely to happen?”, and is characterized by techniques such as regression analysis, multivariate statistics, pattern matching, predictive modeling, and forecasting. Grandview research recently estimated that the global market for predictive analytics is growing at a CAGR of 23.2% and projected to grow to $23.9 Billion by 2025. Initially, the purview of a few visionary companies, predictive analytics is rapidly gathering momentum in the market.  Several industries such as banking, financial services, insurance,…

How Automation Solves the Biggest Pain Points in Data Science

By Walter Paliska

While most of the attention in the world of AI and Machine Learning is on the algorithms themselves, most data scientists often worry not about the outcome, but instead on the steps involved in arriving at that outcome. The reason for this is simple: building AI and ML models is tedious, complicated, requires a multitude of subject matter experts, and is a highly manual process. In our blogs, we have often highlighted the multiple steps necessary to build useful AI and ML models through data science. Today's article focuses on what data science teams can do to accelerate the building of models, while still achieving the goal of building valuable AI/ML models. As a refresher, below is an illustration of the complexity and multi-step nature of the data science process. To understand the benefits of automation in data science, we first have to know where the most manual work is…

AutoML: How Do You Measure Return On Investment?

By Walter Paliska

So your company has decided to invest in an Automated Machine Learning (AutoML) platform. Excellent - AutoML promises that it can help accelerate and automate much of your data science process. At first blush, the return on investment (ROI) for your technology purchase would seem simple: Measure how many data science projects your team could produce on average before your platform purchase, and then measure again afterward. If your results are anything like what our clients have seen, you will likely measure ROI in terms of time: many of our clients are finding that they can deliver data science projects 10X to as much as 32X faster than they could manually. While those numbers are high, however, there are other even more powerful means of measuring ROI that will be even more meaningful and valuable to your business. Leaders should think beyond cost savings and look at developing sustainable competitive…

dotData’s AI-FastStart™ Program Helps BI teams Adopt AI/ML with AutoML 2.0

By Walter Paliska

Today dotData is thrilled to announce dotData AI-FastStart™, our new exclusive program aimed at helping Business Intelligence professionals with the adoption of AI and Machine Learning (ML) powered Business Intelligence (BI) solutions - regardless of the amount of expertise or infrastructure readiness of the organization. With AI-FastStart™, BI teams can quickly move from zero to a fully operational AI/ML experience in ninety days (90) or less. AI-FastStart™ was born as a direct response to a rapidly changing BI & Analytics world. AI/ML has become a critical technology investment but most organizations still suffer from scaling AI/ML practices. BI+AI (a.k.a. citizen data scientists) is no longer a “nice to have” but must become the new approach to scale AI/ML for organizations. dotData AI-FastStart™ makes AI/ML adoption simple, easy and fast.  The program was designed around four core principles: The right platform, education, providing fast time-to-value, and to be easy to deploy…

AutoML 2.0: Is The Data Scientist Obsolete?

By Ryohei Fujimaki, PhD.

As originally seen on Forbes Cognitive World, our CEO - Ryohei Fujimaki PhD was a primary contributor to this article.  In case you've missed it, we've reposted the Original Article below. It's an AutoML WorldThe world of AutoML has been proliferating over the past few years - and with a recession looming, the notion of automating the development of AI and Machine Learning is bound to become even more appealing. New platforms are available with increased capabilities and more automation. The advent of AI-powered Feature Engineering - which allows users to discover and create features for data science processing automatically - is enabling a whole new approach to data science that, seemingly, threatens the role of the data scientist. Should data scientists be concerned about these developments? What is the role of the data scientist in an automated process? How do organizations evolve because of this newfound automation?AutoML 2.0, More Automation for…

What IS Feature Engineering?

By Walter Paliska

What Is Feature Engineering?(And Why Do We Need To Automate it?) The past few years have seen the rapid rise in the adoption of Artificial Intelligence (AI) and Machine Learning (ML) for a multitude of commercial use-cases. Beyond the “cute” factor of AI that can pick a cat out of a photo array, AI and Machine learning are being deployed to model and predict lending risk, to understand and manage customer churn, provide product recommendations, help with programmatic advertising and much more. The challenge for the business community is that the underlying practice that is at the heart of AI and Machine Learning - data science - is rooted in a complex world of statistical analysis, data manipulation, programming and more. Most businesses don’t have enough data scientists - a fact illustrated by research in 2018 by LinkedIn that showed that there would be a shortfall of over 150,000 people…

AutoML and Beyond – Part 2

By dotData

Watch Part 2 (the Conclusion) of "AutoML and Beyond." With AutoML trending in data science, our CEO spoke at #Ai4Finance on data preparation, aggregating tables, feature engineering, the #AutoML process, and AutoML’s missing gaps.  Video: AutoML and Beyond - Part 2 Share On [social_warfare ] Related Articles

AutoML and Beyond – Part 1

By dotData

With AutoML trending in data science, our CEO spoke at #Ai4Finance on data preparation, aggregating tables, feature engineering, the #AutoML process, and AutoML’s missing gaps.  We’ll post the Conclusion / Part 2 next Thursday.  Video: Part 1 – AutoML and Beyond. Share On [social_warfare ] Related Articles

REPOST: Forbes Cognitive World Article – Is Finance Ready for AutoML

By Ryohei Fujimaki, PhD.

Previously Published by: Forbes Cognitive World | Original Post: AI In Financial Services: Is Finance Ready For AutoML AI In Financial Services: Is Finance Ready For AutoML This past month I attended the AI for Finance show in New York where I was the host for a one-hour "tech talk" on the subject of AutoML in the world of financial services. With over 150 people in attendance, the audience was able to provide some pointed input on the topic of AI in Financial Services, and especially around the need for AutoML solutions in the space. The results were both surprising and exciting. A diverse audience Let's start with who was in attendance. While this may not have been a vast audience, the 150+ people that attended our presentation represented a relatively broad cross-sampling including many US top-tier financial services organizations - both from a technical and a business perspective - with…