MLflow for Model Lifecycle Management : Automating AI Pipelines for Efficient Deployment

In the fast-paced world of AI and machine learning, where models evolve from experiments to production at breakneck speed, chaos often reigns: scattered experiments, irreproducible results, and deployment nightmares that waste time and resources. But what if you could streamline the entire model lifecycle into a seamless, automated pipeline that scales effortlessly?
Discover the power of MLflow for Model Lifecycle Management: Automating AI Pipelines for Efficient Deployment by MLOps pioneer Cyrus Laban. This essential 2025 guide unlocks the full potential of MLflow, the open-source platform revolutionizing how teams build, track, and deploy AI models. Whether you're battling version control issues, struggling with cloud integrations, or aiming for continuous deployment, Laban provides a step-by-step roadmap to automate your workflows, ensuring efficiency, reproducibility, and reliability from day one.
Embark on a structured exploration across four in-depth parts, packed with practical insights and real-world applications:

• Introduction to MLflow and the Machine Learning Lifecycle: Tackle core challenges like experiment management, compare MLflow to alternatives, and set up your first tracking server—complete with hands-on installation on local and cloud environments.
• Core MLflow Components for Model Development: Dive into tracking (logging metrics, parameters, and artifacts), projects (for reproducible workflows with Conda and Docker), and models (standardizing packaging across flavors like PyTorch and TensorFlow).
• Model Registry and Deployment: Master version control in the registry, automate promotions via CI/CD, and deploy models locally, to clouds like AWS SageMaker, or via containers—featuring REST APIs and batch inference.
• Advanced Topics and Real-World Applications: Scale with distributed backends, customize with plugins, automate pipelines using Airflow for hyperparameter tuning and retraining, and explore case studies on recommendation systems, fraud detection, and NLP chatbots. Plus, future-proof your setups with trends in MLOps, fairness, and compliance.

What makes this book indispensable? Hands-on exercises in every chapter transform concepts into actionable skills—log your first experiment, package a deep learning model, promote to production, and build automated retraining pipelines using Python code examples and tools like Airflow, Docker, and Kubernetes. Ideal for data scientists, ML engineers, DevOps professionals, and teams scaling AI, this first edition includes appendices on command references, troubleshooting, and further resources to keep you ahead.