MLOps LLMOps And Deployment

Master the complete production lifecycle of Machine Learning and Large Language Models-from experimentation to enterprise deployment.MLOps, LLMOps and Deployment is a practical, engineer-focused guide that takes you beyond model training and into the real-world challenges of deploying, monitoring, scaling, and maintaining AI systems in production.Designed for AI Engineers, Machine Learning Engineers, Data Scientists, MLOps Engineers, DevOps Professionals, Cloud Architects, and students preparing for technical interviews, this book explains every major concept using simple language, production examples, architecture diagrams, troubleshooting scenarios, interview questions, and hands-on workflows.Inside this book, you'll learn: - Complete end-to-end MLOps lifecycle- CI, CD, and Continuous Training (CT)- Model versioning and Model Registry- Drift detection and production monitoring- Docker and Kubernetes for ML deployment- Blue-Green, Canary, Rolling, Shadow, and A/B deployments- LLMOps fundamentals, prompt versioning, and RAG operations- Feature Stores and production data pipelines- Observability, alerting, and incident response- AI governance, compliance, and model security- GitOps with ArgoCD, Flux, and Terraform- AWS SageMaker, Azure ML, and Google Vertex AI implementations- GPU inference optimization using TensorRT, Triton, CUDA, and vLLM- Agentic AI workflows, MCP, Guardrails, and Prompt Registries- Production troubleshooting and enterprise readiness checklistsEvery chapter includes: - Simple explanations- Real production challenges- Architecture diagrams- Practical code examples- Interview Q&A- Knowledge checks- Hands-on exercisesWhether you're deploying your first ML model or building enterprise-scale AI platforms, this book provides the practical knowledge needed to confidently design, deploy, monitor, and operate modern AI systems.