This course provides a comprehensive exploration of machine learning engineering and MLOps using TensorFlow Extended (TFX) and related production tools. Designed for practitioners who want to move beyond experimentation, the course focuses on building reliable, scalable, and maintainable machine learning systems in real-world environments.

Learners will gain a deep understanding of TFX concepts, including pipeline architecture, metadata management, data validation, distributed processing with Apache Beam and Dataflow, and continuous retraining strategies. The course also covers model analysis, fairness considerations, and aligning machine learning outcomes with business realities.

A significant portion of the course is dedicated to deployment and serving. Topics include TensorFlow Serving fundamentals, client integration, customization, performance optimization, and advanced serving features. Learners will also explore best practices for ML deployment, inference at scale, and transitioning models from research to production.

Drawing from expert talks, keynotes, and industry case studies, this course is ideal for ML engineers, data scientists, and technical leaders responsible for production ML systems. By the end of the course, learners will have a clear framework for managing the entire ML lifecycle using modern MLOps principles and TensorFlow tools.