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Move your machine learning models from the notebook to production. It's time to build and manage solutions that deliver real-world impact at scale.

Processing massive datasets is a real engineering challenge. To do it efficiently, you need to master the industry standard for distributed computing: Apache Spark.

Test your knowledge with 50+ questions from the Experimentation section of the Databricks Certified Machine Learning Professional certification. Find out if you’re ready to ace the exam — can you identify the requirements for tracking nested runs, enable autologging (including with Hyperopt), and log and view artifacts such as SHAP plots, custom visualizations, feature data, images, and metadata?

Test your knowledge with 50+ questions from the Model Deployment section of the Databricks Certified Machine Learning Professional certification. Find out if you’re ready to ace the exam — can you identify z-ordering as a solution for reducing the time needed to read predictions from a table, identify partitioning on a common column to speed up queries, explain the practical benefits of using the score_batch operation, describe Structured Streaming as a common tool for ETL pipelines, explain Structured Streaming as a continuous inference solution for incoming data, understand why complex business logic must be handled in streaming deployments, and recognize that data can arrive out of order when using Structured Streaming? And much more!

Test your knowledge with 50+ questions from the Model Deployment section of the Databricks Certified Machine Learning Professional certification. Find out if you’re ready to ace the exam — can you describe continuous predictions in a time-based prediction store as a scenario for streaming deployments, convert a batch deployment pipeline’s inference step to a streaming deployment pipeline, convert a batch deployment pipeline’s writing step to a streaming deployment pipeline, explain the benefits of real-time inference for a small number of records or fast prediction computations, identify JIT feature values as a requirement for real-time deployment, query a Model Serving–enabled model in the Production and Staging stages, and explain why cloud RESTful services in containers are best suited for production-grade real-time deployments? And much more!

Raw data can be messy and unreliable. The modern data engineer transforms that chaos into clean, trusted, and performant data assets ready for analytics and AI. This is where you learn that craft.