MLOps on Azure for Real-World AI: Build, Deploy, and Monitor ML Pipelines with Azure CLI, GitHub, and LLMOps Tools
Build, Automate, and Scale Intelligent ML Pipelines - From Zero to Full Production, with Azure, GitHub, and Real LLMOps Tools
Are you ready to go beyond ML theory and start deploying real, production-grade AI workflows?
"MLOps on Azure for Real-World AI" is your all-in-one, practical blueprint to mastering the full machine learning lifecycle on Azure - from clean code to automated CI/CD, prompt engineering to scalable deployment, real-time inference to monitoring drift and re-training - all powered by the modern MLOps stack and LLMOps best practices.
Whether you're a data scientist, MLOps engineer, cloud architect, or AI product developer, this book walks you step-by-step through building reproducible, secure, and scalable ML/LLM pipelines using Azure CLI, Python SDK, GitHub Actions, Prompt Flow, LangChain, MLflow, Terraform, and more.
What You'll Build InsideReusable ML Pipelines with job.yml, tracking, and GitHub version controlCI/CD Automation using GitHub Actions for training, testing, and deploymentPrompt Flow & LLMOps with Azure OpenAI, streaming templates, and eval toolsReal-Time & Batch Deployments with rollback-ready endpoints and auto-scalingMonitoring & Governance using Azure Monitor, RBAC, lineage, and drift detectionMulti-Cloud Interoperability with Terraform, GCP, AWS, and hybrid GitHub workflowsEnd-to-End Project combining ML and LLM in a single production-grade pipelineHands-On Practice at Every StepEach chapter includes a dedicated Practice Lab so you can apply what you learn in real time - from setting up your first Azure workspace to deploying a LangChain-powered RAG pipeline on Azure ML.
Who This Book Is ForIf you've ever wondered:
How do I operationalize my ML models at scale?How do I integrate LLMs like Azure OpenAI into enterprise pipelines?How do I automate model training, testing, and monitoring with modern DevOps?How do I prepare for real-world production with compliance, versioning, and governance in mind?Then this is the only book you need.
No Fluff. No Theory. Only Real MLOps.Every chapter in this book was written to reflect current best practices and real tools being used by top ML engineering teams across industries. This is not another academic guide - it's a hands-on engineering playbook.
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