Practical Machine Learning Engineering: A Hands-On Guide to Model Development, MLOps, and Production Deployment

A Hands-On Guide to Model Development, MLOps, and Production Deployment
Machine learning is no longer just about building models-it's about building systems.
As organizations push AI into production, engineers must navigate data pipelines, automation frameworks, deployment platforms, monitoring tools, and real-world operational constraints.

Practical Machine Learning Engineering is a complete, hands-on guide to designing, building, and operating modern ML systems at scale. Written for engineers, developers, and MLOps practitioners, this book walks you through the full end-to-end lifecycle, from problem framing to production maintenance.
You'll learn how to structure training workflows, build reproducible pipelines, automate ML operations, evaluate model performance, manage versioning, deploy models with confidence, and maintain reliable inference services. The book features real-world best practices, actionable patterns, and field-tested architectures used across modern AI-driven organizations.
By the end, you'll be equipped with the skills to deliver robust, scalable, and maintainable ML systems-not just working models.
What You'll Learn

• Build reliable ML pipelines using modern tools and automation frameworks
• Manage data, experiments, and model versions with professional discipline
• Apply MLOps principles to streamline model training and deployment
• Implement CI/CD workflows tailored for machine learning systems
• Deploy models using batch, streaming, and real-time inference strategies
• Monitor AI systems in production and detect drift, anomalies, and performance decay
• Build scalable, maintainable ML platforms ready for enterprise environments

Perfect for engineers, data scientists, and technical teams who want to master production-grade machine learning and transform prototypes into real, high-impact systems.