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,…
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…
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,…
