At its re:Invent conference, AWS CEO Andy Jassy today announced the launch of SageMaker Studio, a web-based IDE for building and training machine learning workflows. It includes everything a data scientist would need to get started, including ways to organize notebooks, data sets, code and models, for example. It essentially wants to be a one-stop shop for all the machine learning tools and results you need to get started.
At the core of Studio is also the ability to share projects and folders with others who are working on the same project, including the ability to discuss notebooks and results.
Because you need to train those models, too, the service is obviously integrated with AWS’s SageMaker machine learning service, which can automatically scale based on your needs.
In addition to Studio, AWS also today announced a number of other updates to SageMaker that are integrated into Studio. Most of these run under the hood of Studio, but you also can use them as standalone tools. These include a debugger, a monitoring tool and Autopilot, which automatically creates the best models for you based on your data, with full visibility into how it decides to build your models.
Related to this, AWS also launched SageMaker Notebooks today, which is also integrated into Studio. These are, in essence, notebooks as a managed service. Data scientists won’t have to provision instances for this as they will automatically provision them as necessary.
Ideally, Studio will make building models significantly more accessible to a wider range of developers. AWS calls this the middle-layer of the stack, which is meant for machine learning practitioners who don’t want to delve into all the details but still have a lot of hands-on control.