Sagemaker

A guide to building, training, and deploying machine learning models for developers and data scientists

Packt publisher

This domain evolves: algorithms, infrastructure, frameworks, best practices, experiment with a technique,shipping actual models to actual customers in production. Add debugging, scaling and monitoring to the list.
Up-to-date and having the right set of tools, handling speed efficiently
Streamline yourexperimentation's pipelines, shorten the time from development to production, scale projects removing all the hassle of infrastructure maintenance, setup and updates
AWS storage solutions, Jupyter notebook, automated labelling, automated model debugging, hyper-parameter tuning and experiment handling *. Deployment stage *. Off-the-shelf Docker images, A/B testing, canary deployments capabilities, features' distribution shifts tracking

Francesco Pochetti, Senior Data Scientist at Mash & AWS ML Hero

He learned first programming language 40 years ago. BASIC programming book for the Commodore VIC-20.
I'd also like to thank the AWS service teams who work on Amazon SageMaker every day,You are the unspoken heroes of this book, and I'm but your messenger
Principal AI and Machine learning develoer advocate.
Frequently speak at conferences and blogs on AWS blogs and on Medium
He served 10 years as CTO/VP in top-tier web start-ups he lead software and ops teams
he fought his way through a wide range of technical, business, and procurement issues, which helped him gain a deep understanding of physical infrastructure, its limitations, and how cloud computing can help.