Over the last couple of years, there has been a lot of hype around robotic process automation. This makes a lot of sense if you consider that in 2018 Gartner was already labeling it “…Continue
Added by Daniel Pullen on July 20, 2020 at 4:30am — No Comments
Summary: Too many solutions. We are at an inflection point where too many vendors are offering too many solutions for moving our AI/ML models to production. The very real risk is duplication of effort, fragmentation of our data science resources, and incurring unintended new technical debt as we bind ourselves to platforms that have hidden assumptions or limitations in how that approach problems.
Added by William Vorhies on November 25, 2019 at 9:44am — No Comments
Summary: AI/ML itself is the next big thing for many fields if you’re on the outside looking in. But if you’re a data scientist it’s possible to see those advancements that will propel AI/ML to its next phase of utility.
Summary: Based on a McKinsey study we reported that 47% of companies had at least one AI/ML implementation in place. Looking back at the data and the dominance of RPA as the most widely reported instance makes us think that the number is probably significantly lower.