MLflow MLOps Pipeline Building Practical Workbook: Deploy 30 Models and 5 Monitoring Projects

Unlock the Power of MLOps with MLflow: Your Hands-On Guide to Production-Ready Machine Learning Pipelines
In today's fast-paced AI landscape, turning machine learning models from Jupyter notebooks into scalable, reliable production systems is the ultimate challenge for data scientists and engineers. "MLflow MLOps Pipeline Building Practical Workbook: Deploy 30 Models and 5 Monitoring Projects" by Cyrus Laban is your definitive, action-packed resource to conquer this gap. This isn't just a book—it's a comprehensive workbook designed for real-world application, empowering you to build, deploy, and monitor end-to-end MLOps pipelines using MLflow, the open-source platform revolutionizing ML lifecycle management.
Why does MLOps matter in 2025? As AI drives business innovation, operationalizing models demands reproducibility, automation, and robust monitoring to handle data drift, model performance, and compliance. This book cuts through theory with a relentless focus on practice: you'll deploy 30 diverse models across classification (e.g., fraud detection, churn prediction), regression (e.g., house price forecasting, energy consumption), NLP (e.g., sentiment analysis), and computer vision (e.g., image classification). Plus, tackle 5 in-depth monitoring projects covering model tracking, data and model drift detection, infrastructure observability, and end-to-end pipeline health—ensuring your systems stay performant in dynamic environments.
Structured for progressive mastery:

• Part I: Foundations – Why MLOps is essential, getting started with MLflow, and designing pipelines.
• Part II: Model Building & Deployment – Hands-on with 30 models using algorithms like Logistic Regression, XGBoost, LightGBM, and more.
• Part III: Automation – Integrate CI/CD, Apache Airflow, and Kubernetes for scalable workflows.
• Part IV: Monitoring – Implement 5 projects to detect issues and maintain reliability.
• Part V: Advanced & Future-Proofing – Generative AI, security, trends, and a capstone project to build your own production pipeline.

Cloud-agnostic and adaptable to local setups or AWS/Azure/GCP, this workbook includes step-by-step exercises, code snippets, and a companion GitHub repository with all datasets, scripts, and configurations—eliminating setup hassles so you can dive straight into building. Whether you're preprocessing data, logging experiments, versioning models in the MLflow Registry, or orchestrating deployments, you'll gain the skills to transform ML from experiments to enterprise-grade solutions.
Perfect for intermediate Python users with basic ML knowledge—data scientists productionizing models, DevOps engineers integrating AI, or team leads scaling AI initiatives. No prior MLOps expertise required; the clear explanations, conventions, and appendices (including installation guides and tool references) make it accessible yet deep for advanced users.
By the end, you'll have deployed 30 models, executed 5 monitoring projects, and constructed a full MLOps pipeline—equipped with the confidence to tackle real-world challenges like reducing deployment times, ensuring compliance, and boosting model reliability. Don't let your ML projects stall in development; grab this workbook today and elevate your career in the AI-driven future. Your path to MLOps mastery starts here!