Posted On: Nov 30, 2022
Amazon SageMaker Studio is a fully integrated development environment (IDE) for machine learning (ML) that enables ML practitioners to perform every step of the machine learning workflow, from preparing data to building, training, tuning, and deploying models. Today, we're excited to announce a new capability in SageMaker Studio notebooks that enables automatic conversion of notebook code to production-ready jobs.
When data scientists and developers move their notebooks into production, they manually copy the snippets of code from notebook into a script, package the script with all its dependencies into a container, and then schedule the container to run as a job. In addition, if the job needs to be run on a schedule, they must set up, configure, and manage a continuous integration and continuous delivery (CI/CD) pipeline to automate their deployments. It can take weeks to get all the necessary infrastructure set up, which takes time away from core ML development activities. SageMaker Studio now lets ML practitioners select a notebook and automate it to run as a job in production with just few simple clicks, right from the Studio visual interface. Once a job is scheduled, SageMaker Studio automatically takes the snapshot of entire notebook, packages it along with its dependencies in a container, builds the infrastructure, runs the notebook as an automated job, and de-provisions the infrastructure upon job completion–reducing the time it takes to move a notebook to production from weeks to hours.
This feature is generally available all AWS commercial regions where SageMaker Studio is available. To learn more, see this blog and SageMaker Studio developer guide.