At the re:Invent conference, Amazon CEO Andy Jassy announced the launch of SageMaker Studio. As a web-based machine learning integration development environment (IDE) designed to facilitate user-built and trained workflows. SageMaker Studio includes all the tools data scientists need, including organizing notepads, datasets, code, models, and more, to provide a one-stop service.
(Screenshot via AWS)
Through this integrated development environment (IDE), users can also share resources and discussions with others working on the same project. Researchers can train relevant models and auto-scales on demand, and are tightly integrated with AWS’ SageMaker machine learning services.
In addition to SageMaker Studio, AWS today announced a number of other updates to SageMaker that are now integrated into Studio.
Most of these tools run in the background, but users can also use them as stand-alone tools, including debuggers, monitors, autopilot, and so on. They automatically create the best model based on the data entered, so that users can understand and decide how to build it.
(Pictured from: AWS)
Finally, AWS Notebook today released SageMaker(now integrated into Studio). Essentially, it is a notepad’s managed service. Data scientists do not need to provide an example for this because they can be configured automatically if necessary.
Ideally, Studio can make modeling tools available to more developers. AWS uses it as the middle layer of the stack, designed for machine learning practitioners who don’t want to delve into all the details but still have a lot of hands-on control.