Summary: We are entering a new phase in the practice of data science, the ‘Code-Free’ era. Like all major changes this one has not sprung fully grown but the movement is now large enough that its momentum is clear. Here’s what you need to know.
Summary: Remember when we used to say data is the new oil. Not anymore. Now Training Data is the new oil. Training data is proving to be the single greatest impediment to the wide adoption and creation of deep learning models. We’ll discuss current best practice but more importantly new breakthroughs into fully automated image labeling that are proving to be superior even to hand labeling.
Added by William Vorhies on August 28, 2018 at 7:27am — No Comments
Summary: Deep changes are underway in how data science is practiced and successfully deployed to solve business problems and create strategic advantage. These same changes point to major changes in how data scientists will do their work. Here’s why and how.
Summary: Recently we’ve been profiling Automated Machine Learning (AML) platforms, both of the professional variety, and particularly those proprietary one-click-to-model variety that are being pitched to untrained analysts and line-of-business managers. Since our first article, readers have suggested some additional companies we should look at which are profiled here along with some interesting observations about who is buying and why.
Summary: A year ago we wrote about the emergence of fully automated predictive analytic platforms including some with true One-Click Data-In Model-Out capability. We revisited the five contenders from last year with one new addition and found the automation movement continues to move forward. We also observed some players from last year have now gone in different directions. …Continue
Summary: The shortage of data scientists is driving a growing number of developers to fully Automated Predictive Analytic platforms. Some of these offer true One-Click Data-In-Model-Out capability, playing to Citizen Data Scientists with limited or no data science expertise. Who are these players and what does it mean for the profession of data science?