The Definitive Guide To Machine Learning Operations In Aws
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The Definitive Guide to Machine Learning Operations in AWS
Foreword by Dr. Shreyas Subramanian, Principal Data Scientist, Amazon This book focuses on deploying, testing, monitoring, and automating ML systems in production. It covers AWS MLOps tools like Amazon SageMaker, Data Wrangler, and AWS Feature Store, along with best practices for operating ML systems on AWS. This book explains how to design, develop, and deploy ML workloads at scale using AWS cloud's well-architected pillars. It starts with an introduction to AWS services and MLOps tools, setting up the MLOps environment. It covers operational excellence, including CI/CD pipelines and Infrastructure as code. Security in MLOps, data privacy, IAM, and reliability with automated testing are discussed. Performance efficiency and cost optimization, like Right-sizing ML resources, are explored. The book concludes with MLOps best practices, MLOPS for GenAI, emerging trends, and future developments in MLOps By the end, readers will learn operating ML workloads on the AWS cloud. This book suits software developers, ML engineers, DevOps engineers, architects, and team leaders aspiring to be MLOps professionals on AWS. What you will learn: ● Create repeatable training workflows to accelerate model development ● Catalog ML artifacts centrally for model reproducibility and governance ● Integrate ML workflows with CI/CD pipelines for faster time to production ● Continuously monitor data and models in production to maintain quality ● Optimize model deployment for performance and cost Who this book is for: This book suits ML engineers, DevOps engineers, software developers, architects, and team leaders aspiring to be MLOps professionals on AWS.
The Definitive Guide to Machine Learning Operations in AWS
Foreword by Dr. Shreyas Subramanian, Principal Data Scientist, Amazon This book focuses on deploying, testing, monitoring, and automating ML systems in production. It covers AWS MLOps tools like Amazon SageMaker, Data Wrangler, and AWS Feature Store, along with best practices for operating ML systems on AWS. This book explains how to design, develop, and deploy ML workloads at scale using AWS cloud's well-architected pillars. It starts with an introduction to AWS services and MLOps tools, setting up the MLOps environment. It covers operational excellence, including CI/CD pipelines and Infrastructure as code. Security in MLOps, data privacy, IAM, and reliability with automated testing are discussed. Performance efficiency and cost optimization, like Right-sizing ML resources, are explored. The book concludes with MLOps best practices, MLOPS for GenAI, emerging trends, and future developments in MLOps By the end, readers will learn operating ML workloads on the AWS cloud. This book suits software developers, ML engineers, DevOps engineers, architects, and team leaders aspiring to be MLOps professionals on AWS. What you will learn: ● Create repeatable training workflows to accelerate model development ● Catalog ML artifacts centrally for model reproducibility and governance ● Integrate ML workflows with CI/CD pipelines for faster time to production ● Continuously monitor data and models in production to maintain quality ● Optimize model deployment for performance and cost Who this book is for: This book suits ML engineers, DevOps engineers, software developers, architects, and team leaders aspiring to be MLOps professionals on AWS.
Amazon Web Services: the Definitive Guide for Beginners and Advanced Users
Author: Parul Dubey
language: en
Publisher: Bentham Science Publishers
Release Date: 2023-10-19
Amazon Web Services: A Comprehensive Guide for Beginners and Advanced Users is your go-to companion for learning and mastering AWS. It presents 10 easy-to-read chapters that build a foundation for cloud computing while also equipping readers with the skills necessary to use AWS for commercial projects. Readers will learn how to use AWS cloud computing services for seamless integrations, effective monitoring, and optimizing cloud-based web applications. What you will learn from this guide: 1. Identity and Access Management in AWS: Learn about IAM roles, security of the root account, and password policies, ensuring a robust foundation in access management. 2. Amazon EC2 Instance: Explore the different types of EC2 instances, pricing strategies, and hands-on experiences to launch, manage, and terminate EC2 instances effectively. This knowledge will help to make informed choices about pricing strategies. 3. Storage Options and Solutions: A detailed examination of storage options within Amazon EC2 instances. Understanding Amazon Elastic Block Store (EBS), Amazon Elastic File Storage (EFS), and more, will enhance your ability to handle data storage efficiently. 4. Load Balancing and Auto Scaling: Learn about different types of load balancers and how auto-scaling groups operate, to master the art of managing varying workloads effectively. 5. Amazon Simple Storage Service (S3): Understand S3 concepts such as buckets, objects, versioning, storage classes, and practical applications. 6. AWS Databases and Analytics: Gain insights into modern databases, AWS cloud databases, and analytics services such as Amazon Quicksight, AWS Glue, and Amazon Redshift. 7. Compute Services and Integrations: Understand the workings of Docker, virtual machines, and various compute services offered by AWS, including AWS Lambda and Amazon Lightsail, Amazon MQ and Amazon SQS. 8. Cloud Monitoring: Understand how to set up alarms, analyze metrics, and ensure the efficient monitoring of your cloud environment using Amazon CloudWatch and CloudTrail. Key Features: Comprehensive Introduction to Cloud Computing and AWS Guides readers to the complete set of features in AWS Easy-to-understand language and presentation with diagrams and navigation guides References for further reading Whether you're a student diving into cloud specialization as part of your academic curriculum or a professional seeking to enhance your skills, this guide provides a solid foundation for learning the potential of the AWS suite of applications to deploy cloud computing projects.