Sireesha Muppala, Randy Defauw, Shelbee Eigenbrode

Amazon SageMaker Best Practices (eBook, ePUB)

Proven tips and tricks to build successful machine learning solutions on Amazon SageMaker

• Format: ePub

Produktbeschreibung

Amazon SageMaker is a fully managed AWS service that provides the ability to build, train, deploy, and monitor machine learning models. The book begins with a high-level overview of Amazon SageMaker capabilities that map to the various phases of the machine learning process to help set the right foundation. You'll learn efficient tactics to address data science challenges such as processing data at scale, data preparation, connecting to big data pipelines, identifying data bias, running A/B tests, and model explainability using Amazon SageMaker. As you advance, you'll understand how you can tackle the challenge of training at scale, including how to use large data sets while saving costs, monitoring training resources to identify bottlenecks, speeding up long training jobs, and tracking multiple models trained for a common goal. Moving ahead, you'll find out how you can integrate Amazon SageMaker with other AWS to build reliable, cost-optimized, and automated machine learning applications. In addition to this, you'll build ML pipelines integrated with MLOps principles and apply best practices to build secure and performant solutions.
By the end of the book, you'll confidently be able to apply Amazon SageMaker's wide range of capabilities to the full spectrum of machine learning workflows.

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Produktdetails

• Verlag: Packt Publishing
• Seitenzahl: 348
• Erscheinungstermin: 24. September 2021
• Englisch
• ISBN-13: 9781801077767
• Artikelnr.: 70295666

Autorenporträt

Sireesha Muppala, PhD is a Principal Enterprise Solutions Architect, AI/ML at Amazon Web Services (AWS). Sireesha holds a PhD in computer science and post-doctorate from the University of Colorado. She is a prolific content creator in the ML space with multiple journal articles, blogs, and public speaking engagements. Sireesha is a co-creator and instructor of the Practical Data Science specialization on Coursera. She is a co-director of Women In Big Data (WiBD), Denver chapter. Sireesha enjoys helping organizations design, architect, and implement ML solutions at scale.