MLOps practices help data scientists and IT operations professionals collaborate and manage the production ML workflow, including data preparation and building and training, deploying, and monitoring models. During this session, explore the features in Amazon SageMaker Pipelines that help you increase automation, track data lineage, catalog ML models for production, improve the quality of your end-to-end workflows, and support governance. Also, learn how to use SageMaker projects, which provide MLOps templates for incorporating CI/CD practices into your ML pipelines.
