Engineer Features and Evaluate Models for Production

Engineer Features and Evaluate Models for Production is an intermediate course for machine learning practitioners and data scientists who are ready to move beyond notebooks and build production-grade ML systems. Getting a model to work once is easy; making it reliable, reproducible, and efficient in production is the real challenge. This course provides the engineering discipline to bridge that gap. You will learn to build robust, reproducible feature engineering pipelines using scikit-learn's ColumnTransformer to handle mixed data types—numeric, categorical, and text—in a single, elegant workflow. Then, you will move beyond simple accuracy scores and learn to evaluate experiments like a seasoned MLOps professional. Using TensorBoard, you will inspect training and validation curves to diagnose issues such as overfitting, analyze performance trade-offs, and make data-driven decisions. The course culminates in a comprehensive Feature Engineering and Evaluation Report, where you will apply your skills to select a production-ready model. By the end, you will not only be building models, but also be capable of engineering reliable, efficient, and production-worthy ML systems

• Build reproducible feature engineering pipelines using scikit-learn
• Evaluate ML experiments with MLOps tools
• Analyze training issues like overfitting with TensorBoard
• Create comprehensive evaluation reports for production model selection

• Basic familiarity with ML concepts and software tools
• Readiness for applied exercises and practice