Learn from AWS Machine Learning Hero Vinicius Caridá how Amazon SageMaker enables data scientists and IT operations professionals to collaborate and manage ML workflow in production throughout the ML lifecycle including, data preparation, creation, training, deployment and monitoring models.

Learning Objectives:
* Objective 1: Learn how to use Amazon SageMaker to efficiently collaborate with teams across all steps of the ML lifecycle.
* Objective 2: Learn how to automate the different steps of the ML workflow, including data loading, data transformation, training and tuning, and deployment.
* Objective 3: Learn how to create, automate, and manage end-to-end ML workflows at scale.

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