Machine Learning with Amazon SageMaker is a hands-on workshop designed to provide data scientists and developers with end-to-end understanding of the Amazon SageMaker platform, from handling data and features, to building, training, tuning, and deploying ML models. The workshop consists of theoretical modules and hands-on labs that focus on data quality, feature engineering, SageMaker built-in algorithms, and advanced concepts like model deployment on XGBoost.

Agenda (Pacific Time):

* 9:00 AM - 9:15 AM: Welcome & Introductions
* 9:15 AM - 10:15 AM: Session: "Introduction to Amazon SageMaker and Data for Machine Learning"
* 10:15 AM - 11:00 AM: Lab: "Feature Engineering with Amazon SageMaker"
* 11:00 AM - 11:15 AM: Break
* 11:15 AM - 12:15 AM: Session: "Create Model, Prediction and Inference with Amazon SageMaker"
* 12:15 PM - 12:45 PM: Lunch
* 12:45 PM - 1:15 PM: Lab: "Train, Tune and Deploy Model using Amazon SageMaker Built-in Algorithm"
* 1:15 PM - 1:30 PM: Wrap-up / Q&A

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Why Attend?

* Learn how to build, train, tune, and deploy ML models with Amazon SageMaker in production-like scenarios
* Explore best practices for using Amazon SageMaker’s built-in algorithms in ML production systems
* Learn AWS services for managing data, features, ML models, and projects through hands-on experience
* Address your questions about doing ML in the cloud to experts from AWS and Provectus

Who Should Attend

Data Scientists, ML Engineers, MLOps and QA professionals, and developers. Basic familiarity with AWS is recommended.