Data Engineering for AI and ML Pipelines equips you with the skills to build the data infrastructure that powers modern machine learning systems on Databricks. Across three courses, you will progress from foundational data engineering with Apache Spark and PySpark, through Delta Lake and Medallion Architecture pipelines, to feature engineering and feature stores that supply clean, AI-ready data directly to ML workflows.
By the end of this specialization, you will be able to design end-to-end data pipelines using Bronze, Silver, and Gold layers, enforce schema and data quality at scale, build and query feature stores for both structured and text/embedding data, and automate pipeline orchestration using Databricks Jobs and MLflow. This specialization is ideal for aspiring data engineers, machine learning engineers, and data professionals who want to master the full journey from raw data to ML-ready features.
Übungsprojekt
Throughout this specialization, you will build a complete, production-style data pipeline for machine learning across three courses. In Course 1, you will ingest and process data using Apache Spark and PySpark on Databricks. In Course 2, you will design a multi-layer Medallion Architecture pipeline — Bronze, Silver, Gold — using Delta Lake, applying ACID transactions and streaming reliability checks. In Course 3, you will build and query a feature store, including text and embedding features for AI workloads, then automate the pipeline using Databricks Jobs and MLflow. By the end, you will have built a complete pipeline feeding directly into machine learning workflows.
