AWS SageMaker’s new machine learning IDE isn’t ready to win over data scientists

AWS CEO Andy Jassy introduces SageMaker Studio on-stage Tuesday at AWS reInvent.

AWS SageMaker, the machine learning brand of AWS, announced the release of SageMaker Studio, branded an “IDE for ML,” on Tuesday. Machine-learning has been gaining traction and, with its compute-heavy training workloads, could prove a decisive factor in the growing battle over public cloud. So what does this new IDE mean for AWS and the public cloud market?

First, the big picture (skip below for the feature by feature analysis of Studio): It’s no secret that SageMaker’s market share is minuscule (the Information put it around $11 million in July of 2019). SageMaker Studio attempts to solve important pain points for data scientists and machine-learning (ML) developers by streamlining model training and maintenance workloads. However, its implementation falls short due to commonlong-standing, complaints about AWS in general — its steep learning curve and sheer complexity.

AWS is clearly embracing a strategy of selling to corporate IT while neglecting features and UX that could make life easier for data scientists and developers. While the underlying technologies they are releasing, like Notebooks, Debugger, and Model Monitor attempt to make ML training easier, the implementations leave a lot to be desired.