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Full Description
A practical guide to building, deploying, automating, monitoring, and scaling ML and LLM solutions in production.
Key Features
Build reproducible ML pipelines with Azure ML CLI and GitHub Actions
Automate ML workflows end to end, including deployment and monitoring
Apply LLMOps principles to deploy and manage generative AI responsibly across clouds
Purchase of the print or Kindle book includes a free PDF eBook
Book DescriptionEffective machine learning (ML) now demands not just building models, but deploying and managing them at scale. Written by a seasoned senior software engineer with high-level expertise in both MLOps and LLMOps, MLOps for DevOps and Cloud Engineers equips ML practitioners, DevOps engineers, and cloud professionals with the skills to automate, monitor, and scale ML systems across environments.
The book begins with MLOps fundamentals and their roots in DevOps, exploring training workflows, model versioning, and reproducibility using pipelines. You'll implement CI/CD with GitHub Actions and Azure ML CLI, automate deployments, and manage governance and alerting for enterprise use. The author draws on their production ML experience to provide you with actionable guidance and real-world examples. A dedicated section on LLMOps covers operationalizing large language models (LLMs) like GPT-4 using RAG patterns, evaluation techniques, and responsible AI practices. You'll also work with case studies across Azure, AWS, and GCP that offer practical context for multi-cloud operations.
Whether you're building pipelines, packaging models, or deploying LLMs, this guide delivers end-to-end strategy to build robust, scalable systems. By the end of this book, you'll be ready to design, deploy, and maintain enterprise-grade ML solutions with confidence.What you will learn
Understand the DevOps to MLOps transition
Build reproducible, reusable pipelines using Azure ML CLI
Set up CI/CD for training and deployment workflows
Monitor ML applications and detect model/data drift
Capture and secure governance and lineage data
Operationalize LLMs using RAG and prompt flows
Apply MLOps across Azure, AWS, and GCP use cases
Who this book is forThis book is for DevOps and Cloud engineers and SREs interested in or responsible for managing the lifecycle of machine learning models. Professionals who are already familiar with their ML workloads and want to improve their practices, or those who are new to MLOps and want to learn how to effectively manage machine learning models in this environment, will find this book beneficial. The book is also useful for technical decision-makers and project managers looking to understand the process and benefits of MLOps.
Contents
Table of Contents
Understanding the Journey from DevOps to MLOps
Training and Experimentation
Reproducibility and Reusability
Model Management (Registration, Packaging)
Model deployment Batch Scoring and Real-time Web Services
Capturing and Securing Governance Data
Monitoring ML Applications
Working with Notification and Alerting
Automating the ML Lifecycle with ml Pipelines and GitHub Workflows
Using Models in Real-world Application
Exploring nextGen MLOps
