Harness the power of MLOps for managing real time Machine Learning project cycle. MLOps is the intersection of DevOps, data engineering and Machine Learning. Working in the field of machine learning is highly dependent on ever-changing data, whereas MLOps is needed to deliver excellent ML and AI results. This book provides a practical guide to MLOps for data scientists, data engineers, and other professionals involved in building and deploying Machine Learning systems. It introduces MLOps, explaining its core concepts like continuous integration and delivery for Machine Learning. It outlines MLOps components and architecture, providing an understanding of how MLOps supports robust ML systems that continuously improve. By covering the end-to-end Machine Learning pipeline from data to deployment, the book helps readers implement MLOps workflows. It discusses techniques like feature engineering, model development, AB testing, and canary deployments. The book equips readers with knowledge of MLOps tools and infrastructure for tasks like model tracking, model governance, metadata management, and pipeline orchestration. Monitoring and maintenance processes to detect model degradation are covered in depth. With its comprehensive coverage and practical focus, this book enables data scientists, data engineers, DevOps engineers, and technical leaders to effectively leverage MLOps.