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This is the code repository for Getting Started with Amazon SageMaker Studio, published by Packt.

Learn to build end-to-end machine learning projects in the SageMaker machine learning IDE

Amazon SageMaker Studio is the first integrated development environment (IDE) for machine learning (ML) and is designed to integrate ML workflows: data preparation, feature engineering, statistical bias detection, automated machine learning (AutoML), training, hosting, ML explainability, monitoring, and MLOps in one environment.

This book covers the following exciting features:

• Explore the ML development life cycle in the cloud
• Understand SageMaker Studio features and the user interface
• Build a dataset with clicks and host a feature store for ML
• Train ML models with ease and scale
• Create ML models and solutions with little code
• Host ML models in the cloud with optimal cloud resources
• Ensure optimal model performance with model monitoring
• Apply governance and operational excellence to ML projects

If you feel this book is for you, get your copy today!

All of the code is organized into folders.

Following is what you need for this book: This book is for data scientists and machine learning engineers who are looking to become well-versed with Amazon SageMaker Studio and gain hands-on machine learning experience to handle every step in the ML lifecycle, including building data as well as training and hosting models. Although basic knowledge of machine learning and data science is necessary, no previous knowledge of SageMaker Studio and cloud experience is required.

With the following software and hardware list you can run all code files present in the book (Chapter 1-11).

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.

• Machine Learning with Amazon SageMaker Cookbook [Packt] [Amazon]

• Amazon SageMaker Best Practices [Packt] [Amazon]

Michael Hsieh is a senior AI/machine learning (ML) solutions architect at Amazon Web Services. He creates and evangelizes for ML solutions centered around Amazon SageMaker. He also works with enterprise customers to advance their ML journeys. Prior to working at AWS, Michael was an advanced analytic consultant creating ML solutions and enterprise-level ML strategies at Slalom Consulting in Philadelphia, PA. Prior to consulting, he was a data scientist at the University of Pennsylvania Health System, focusing on personalized medicine and ML research. Michael has two master's degrees, one in applied physics and one in robotics. Originally from Taipei, Taiwan, Michael currently lives in Sammamish, WA, but still roots for the Philadelphia Eagles.

If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.

https://packt.link/free-ebook/9781801070157