Overview

LEARN MLflow - Manage Machine Learning Pipelines and Models Efficiently This book offers a technical and practical approach for professionals looking to master MLflow - one of the leading platforms for managing the machine learning model lifecycle. The content covers everything from environment setup to production deployment, with a strong focus on reproducibility, versioning, tracking, and technical governance. Each chapter presents a key MLflow component (Tracking, Projects, Models, Model Registry) and demonstrates how to apply it in real-world scenarios, with clear examples, progressive structure, and established best practices. More than an introduction, this is a professional operations guide. Throughout the chapters, you will learn how to build auditable pipelines, automate CI/CD integrations, manage model versions in production, analyze metrics, and plan for scalability. Integrations with AutoML, Spark, cloud environments, security validation, access control, and post-deployment monitoring are also explored. The content was developed based on the TECHWRITE 2.2 protocol, ensuring immediate applicability in corporate and technical environments. Ideal for data engineers, data scientists, MLOps architects, and technical leaders seeking to raise the standard of model delivery in real-world environments. MLflow, MLOps, model management, experiment tracking, model deployment.

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