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Amazon SageMaker Best Practices

You're reading from   Amazon SageMaker Best Practices Proven tips and tricks to build successful machine learning solutions on Amazon SageMaker

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Product type Paperback
Published in Sep 2021
Publisher Packt
ISBN-13 9781801070522
Length 348 pages
Edition 1st Edition
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Authors (3):
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Randy DeFauw Randy DeFauw
Author Profile Icon Randy DeFauw
Randy DeFauw
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.
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Shelbee Eigenbrode Shelbee Eigenbrode
Author Profile Icon Shelbee Eigenbrode
Shelbee Eigenbrode
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Shelbee Eigenbrode is a Principal AI and ML Specialist Solutions Architect at AWS. She holds six AWS certifications and has been in technology for 23 years, spanning multiple industries, technologies, and roles. She is currently focusing on combining her DevOps and ML background to deliver and manage ML workloads at scale. With over 35 patents granted across various technology domains, she has a passion for continuous innovation and using data to drive business outcomes. Shelbee co-founded the Denver chapter of Women in Big Data.
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Sireesha Muppala Sireesha Muppala
Author Profile Icon Sireesha Muppala
Sireesha Muppala
LinkedIn
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.
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Table of Contents (20) Chapters Close

Preface 1. Section 1: Processing Data at Scale
2. Chapter 1: Amazon SageMaker Overview FREE CHAPTER 3. Chapter 2: Data Science Environments 4. Chapter 3: Data Labeling with Amazon SageMaker Ground Truth 5. Chapter 4: Data Preparation at Scale Using Amazon SageMaker Data Wrangler and Processing 6. Chapter 5: Centralized Feature Repository with Amazon SageMaker Feature Store 7. Section 2: Model Training Challenges
8. Chapter 6: Training and Tuning at Scale 9. Chapter 7: Profile Training Jobs with Amazon SageMaker Debugger 10. Section 3: Manage and Monitor Models
11. Chapter 8: Managing Models at Scale Using a Model Registry 12. Chapter 9: Updating Production Models Using Amazon SageMaker Endpoint Production Variants 13. Chapter 10: Optimizing Model Hosting and Inference Costs 14. Chapter 11: Monitoring Production Models with Amazon SageMaker Model Monitor and Clarify 15. Section 4: Automate and Operationalize Machine Learning
16. Chapter 12: Machine Learning Automated Workflows 17. Chapter 13:Well-Architected Machine Learning with Amazon SageMaker 18. Chapter 14: Managing SageMaker Features across Accounts 19. Other Books You May Enjoy

References

For additional reading material, please review these references:

  • Learn Amazon SageMaker: A guide to building, training, and deploying ML models for developers and data scientists:

    https://www.amazon.com/Learn-Amazon-SageMaker-developers-scientists/dp/180020891X/ref=sr_1_1?dchild=1&keywords =Learn+Amazon+SageMaker+%3A+A+guide+to+building%2C+training%2C+and+deploying+machine+learning+models+for +developers+and+data+scientists&qid=1624801601&sr=8-1

  • Multi-GPU and distributed training using Horovod in Amazon SageMaker Pipe mode:

    https://aws.amazon.com/blogs/machine-learning/multi-gpu-and-distributed-training-using-horovod-in-amazon-sagemaker-pipe-mode/

  • Streamline modeling with Amazon SageMaker Studio and the Amazon Experiments SDK:

    https://aws.amazon.com/blogs/machine-learning/streamline-modeling-with-amazon-sagemaker-studio-and-amazon-experiments-sdk

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Authors (3)

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Profile icon Muppala
Muppala
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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.
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See other products by Muppala
Profile icon Eigenbrode
Eigenbrode
Github icon
Shelbee Eigenbrode is a Principal AI and ML Specialist Solutions Architect at AWS. She holds six AWS certifications and has been in technology for 23 years, spanning multiple industries, technologies, and roles. She is currently focusing on combining her DevOps and ML background to deliver and manage ML workloads at scale. With over 35 patents granted across various technology domains, she has a passion for continuous innovation and using data to drive business outcomes. Shelbee co-founded the Denver chapter of Women in Big Data.
Read more
See other products by Eigenbrode
Profile icon DeFauw
DeFauw
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.
Read more
See other products by DeFauw
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