AWS Announces Eight New Amazon SageMaker Capabilities - Seite 3

Next-generation Notebooks

Amazon SageMaker Studio Notebook gives practitioners a fully managed notebook experience, from data exploration to deployment. As teams grow in size and complexity, dozens of practitioners may need to collaboratively develop models using notebooks. AWS continues to offer the best notebook experience for users with the launch of three new features that help customers coordinate and automate their notebook code.

• Simplified data preparation: Practitioners want to explore datasets directly in notebooks to spot and correct potential data-quality issues (e.g., missing information, extreme values, skewed datasets, and biases) as they prepare data for training. Practitioners can spend months writing boilerplate code to visualize and examine different parts of their dataset to identify and fix problems. Amazon SageMaker Studio Notebook now offers a built-in data preparation capability that allows practitioners to visually review data characteristics and remediate data-quality problems in just a few clicks—all directly in their notebook environment. When users display a data frame (i.e., a tabular representation of data) in their notebook, Amazon SageMaker Studio Notebook automatically generates charts to help users identify data-quality issues and suggests data transformations to help fix common problems. Once the practitioner selects a data transformation, Amazon SageMaker Studio Notebook generates the corresponding code within the notebook so it can be repeatedly applied every time the notebook is run.
• Accelerate collaboration across data science teams: After data has been prepared, practitioners are ready to start developing a model—an iterative process that may require teammates to collaborate within a single notebook. Today, teams must exchange notebooks and other assets (e.g., models and datasets) over email or chat applications to work on a notebook together in real time, leading to communication fatigue, delayed feedback loops, and version-control issues. Amazon SageMaker now gives teams a workspace where they can read, edit, and run notebooks together in real time to streamline collaboration and communication. Teammates can review notebook results together to immediately understand how a model performs, without passing information back and forth. With built-in support for services like BitBucket and AWS CodeCommit, teams can easily manage different notebook versions and compare changes over time. Affiliated resources, like experiments and ML models, are also automatically saved to help teams stay organized.
• Automatic conversion of notebook code to production-ready jobs: When practitioners want to move a finished ML model into production, they usually copy snippets of code from the notebook into a script, package the script with all its dependencies into a container, and schedule the container to run. To run this job repeatedly 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. Amazon SageMaker Studio Notebook now allows practitioners to select a notebook and automate it as a job that can run in a production environment. Once a notebook is selected, Amazon SageMaker Studio Notebook takes a snapshot of the entire notebook, packages its dependencies in a container, builds the infrastructure, runs the notebook as an automated job on a schedule set by the practitioner, and deprovisions the infrastructure upon job completion, reducing the time it takes to move a notebook to production from weeks to hours.

To begin using the next generation of Amazon SageMaker Studio Notebooks and these new capabilities, visit aws.amazon.com/sagemaker/notebooks.