This course focuses on preparing AI-ready data through feature engineering, feature management, and pipeline automation. You will learn how data engineers create high-quality features, organise reusable feature assets, and automate workflows that support scalable machine learning systems.

You will begin by exploring the principles of feature engineering and learn how to transform raw datasets into meaningful features for machine learning. Through practical exercises, you will create numerical, categorical, and derived features while applying techniques such as scaling, encoding, and skewness handling to improve model performance. Next, you will discover how Feature Stores enable consistent and reusable feature management across AI projects. You will design feature table schemas, manage structured and text-based features, generate embeddings, and store AI-ready features in Databricks for efficient reuse across multiple machine learning workflows. You will also learn how machine learning workflows consume engineered data by preparing training, validation, and test datasets, while using MLflow to track datasets, experiments, and model development for reproducibility and collaboration. Finally, you will automate end-to-end AI/ML data pipelines using Databricks Jobs. You will structure notebook-based workflows into production pipelines, schedule and monitor multi-task jobs, and orchestrate reliable data engineering processes that support enterprise-scale AI applications. By the end of this course, you will be able to: - Engineer high-quality features for machine learning applications. - Build and manage reusable Feature Stores in Databricks. - Prepare and track ML datasets using MLflow. - Automate AI/ML workflows using Databricks Jobs. - Develop scalable data pipelines for production AI systems. Designed for data engineers, machine learning engineers, data scientists, and AI professionals, this course equips you with the practical skills to build feature-driven, automated, and production-ready AI/ML data pipelines using modern data engineering practices.