There is a growing need for professionals with experience in working on machine learning (ML) engineering requirements as well as those with knowledge of automating complex MLOps pipelines in the cloud. This book explores a variety of AWS services, such as Amazon Elastic Kubernetes Service, AWS Glue, AWS Lambda, Amazon Redshift, and AWS Lake Formation, which ML practitioners can leverage to meet various data engineering and ML engineering requirements in production.
This machine learning book covers the essential concepts as well as step-by-step instructions that are designed to help you get a solid understanding of how to manage and secure ML workloads in the cloud. As you progress through the chapters, you’ll discover how to use several container and serverless solutions when training and deploying TensorFlow and PyTorch deep learning models on AWS. You’ll also delve into proven cost optimization techniques as well as data privacy and model privacy preservation strategies in detail as you explore best practices when using each AWS.
By the end of this AWS book, you'll be able to build, scale, and secure your own ML systems and pipelines, which will give you the experience and confidence needed to architect custom solutions using a variety of AWS services for ML engineering requirements.

Osoby które kupowały "Machine Learning Engineering on AWS", wybierały także:

• Windows Media Center. Domowe centrum rozrywki 66,67 zÅ‚, (8,00 zÅ‚ -88%)
• Ruby on Rails. Ćwiczenia 18,75 zÅ‚, (3,00 zÅ‚ -84%)
• DevOps w praktyce. Kurs video. Jenkins, Ansible, Terraform i Docker 190,00 zÅ‚, (39,90 zÅ‚ -79%)
• Przywództwo w Å›wiecie VUCA. Jak być skutecznym liderem w niepewnym Å›rodowisku 58,64 zÅ‚, (12,90 zÅ‚ -78%)
• Scrum. O zwinnym zarzÄ…dzaniu projektami. Wydanie II rozszerzone 58,64 zÅ‚, (12,90 zÅ‚ -78%)

Spis treści

Machine Learning Engineering on AWS. Build, scale, and secure machine learning systems and MLOps pipelines in production eBook -- spis treści

• 1. Introduction to ML Engineering on AWS
• 2. Deep Learning AMIs
• 3. Deep Learning Containers
• 4. Serverless Data Management on AWS
• 5. Pragmatic Data Processing and Analysis
• 6. SageMaker Training and Debugging Solutions
• 7. SageMaker Deployment Solutions
• 8. Model Monitoring and Management Solutions
• 9. Security, Governance, and Compliance Strategies
• 10. Machine Learning Pipelines with Kubeflow on Amazon EKS
• 11. Machine Learning Pipelines with SageMaker Pipelines