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

Język publikacji: angielskim
Autorzy:
Sireesha Muppala, Randy DeFauw, Shelbee Eigenbrode
Amazon SageMaker Best Practices. Proven tips and tricks to build successful machine learning solutions on Amazon SageMaker Sireesha Muppala, Randy DeFauw, Shelbee Eigenbrode - okladka książki

Amazon SageMaker Best Practices. Proven tips and tricks to build successful machine learning solutions on Amazon SageMaker Sireesha Muppala, Randy DeFauw, Shelbee Eigenbrode - okladka książki

Wydawnictwo:
Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
348
Dostępne formaty:
     PDF
     ePub
     Mobi

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Do przechowalni

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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O autorach książki

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.
Randy DeFauw is a Principal Solution Architect at AWS. He holds an MSEE from the University of Michigan, where his graduate thesis focused on computer vision for autonomous vehicles. He also holds an MBA from Colorado State University. Randy has held a variety of positions in the technology space, ranging from software engineering to product management. He entered the big data space in 2013 and continues to explore that area. He is actively working on projects in the ML space, including reinforcement learning. He has presented at numerous conferences, including GlueCon and Strata, published several blogs and white papers, and contributed many open source projects to GitHub.

Shelbee Eigenbrode jest główną architektką rozwiązań w zakresie generatywnej AI w AWS. Posiada ponad 35 patentów z różnych dziedzin techniki.

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