LEARN AWS BEDROCK: Build, Protect, and Scale LLMs with Professional Automation on AWS

This book is ideal for students, data engineers, data scientists, cloud architects, and developers who want to master the implementation, protection, and scalability of Large Language Models (LLMs) in the AWS ecosystem. With a professional approach and practical focus, the content covers Bedrock environment structuring, automation of machine learning pipelines, advanced integration of services such as SageMaker, Lambda, Glue, Redshift, Kinesis, and S3, in addition to strategies for security, compliance, and corporate governance. Learn how to provision secure environments, control costs, manage sensitive data, execute optimized deployments, version models, and operate CI/CD automation in critical AI projects. Discover how to orchestrate multi-cloud integrations, apply performance tuning, monitor workloads, automate troubleshooting, log experiments, and ensure complete operational auditing.

Includes:

• Structuring and automation of AWS Bedrock environments for LLMs

• Governance, compliance, encryption, and sensitive data protection

• CI/CD pipelines, automated deployment, and production rollback

• Integration with SageMaker, Lambda, Glue, Redshift, Kinesis, and S3

• Model versioning, technical documentation, and operational auditing

• Automation strategies, tuning, monitoring, and performance analysis

• Multi-cloud orchestration, integration with external APIs, and intelligent workflows

By the end, you will be able to build, protect, and scale generative AI solutions with LLMs on AWS, adding professional value and positioning yourself at the forefront of corporate artificial intelligence projects.

bedrock, aws, pytorch, tensorflow, scikit learn, machine learning, artificial intelligence, llms, sagemaker, automation, pipelines, ci/cd, governance, security, cloud computing