Databricks ML Professional case studies
Databricks ML Professional · Put your knowledge to work in context.
The Databricks Certified Machine Learning Professional is a proctored certification exam that assesses a candidate's ability to design, implement, and manage enterprise-scale machine learning solutions using advanced Databricks platform capabilities. It sits above the associate-level ML credential and targets practitioners who already build production ML systems: data scientists and ML engineers working with SparkML pipelines, distributed training and hyperparameter tuning, advanced MLflow features, and Feature Store concepts for automated feature pipelines. The Databricks ML Professional case studies are 5 cases and about 2,968 words.
1Churn Feature Pipeline: Skew, Leakage, and Feature Governance
A retail bank trains a 90-day churn model on 60 million customer-month rows stored in Delta Lake. The feature notebook joins a 2.8 TB events table to a 4 million-row account dimension, then creates 350 features.
634 words, about 3 min2Hyperparameter Tuning Under OOM and Holdout Reuse
An ML team tunes an XGBoost classifier for loan default on 2 million rows and 120 features, including 18 categorical fields; one field has 50,000 distinct merchant codes. They launch 400 Hyperopt trials with SparkTrials on an 8-worker cluster, parallelism set to 8.
620 words, about 3 min3Promotion Governance in Unity Catalog and CI/CD
A data platform supports 20 data scientists and six production models. Promotion is manual: a scientist registers a model version in the workspace registry, edits a notebook to point the production job at the new version, and messages the platform team.
571 words, about 3 min4Drift Alerts, Delayed Labels, and Retraining Policy
A lender runs a credit approval model on 300,000 applications per month. Requests and responses are logged to an inference table.
556 words, about 3 min5Real-Time Fraud Endpoint: Capacity, Features, and Canary
A payments company deploys a fraud model as a real-time Databricks Model Serving endpoint. Peak traffic is 2,000 requests per second; overnight it falls to 150.
587 words, about 3 min