Amazon SageMaker Pipelines allows data scientists and machine learning (ML) engineers to automate training workflows, which helps you create a repeatable process to orchestrate model development steps for rapid experimentation and model retraining. You can automate the entire model build workflow, including data preparation, feature engineering, model training, model tuning, and model validation, and catalog it in the model registry. You can configure pipelines to run automatically at regular intervals or when certain events are triggered, or you can run them manually as needed.
In this post, we highlight some of the enhancements to the Amazon SageMaker SDK and introduce new features of Amazon SageMaker Pipelines that make it easier for ML practitioners to build and train ML models.
Pipelines continues to innovate its developer experience, and with these recent releases, you can now use the service in a more customized way:

2.99.0, 2.101.1, 2.102.0, 2.104.0 – Updated

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