A closer look at SageMaker Studio, AWS' machine learning IDE

Back in December, when AWS launched its new machine learning IDE, SageMaker Studio, we wrote up a "hot-off-the-presses" review. At the time, we felt the platform fell short, but we promised to publish an update after working with AWS to get more familiar with the new capabilities. When Amazon launched SageMaker Studio, they made clear the pain points they were aiming to solve: "The machine learning development workflow is still very iterative, and is challenging for developers to manage due to the relative immaturity of ML tooling." The machine learning workflow -- from data ingestion, feature engineering, and model selection to debugging, deployment, monitoring, and maintenance, along with all the steps in between -- can be like trying to tame a wild animal. To solve this challenge, big tech companies have built their own machine learning and big data platforms for their data scientists to use: Uber has Michelangelo, Facebook (and likely Instagram and WhatsApp) has FBLearner flow, Google has TFX, and Netflix has both Metaflow and Polynote (the latter has been open sourced).