DE MLO case studies
DE MLO · Put your knowledge to work in context.
The Databricks Certified Machine Learning Professional (often searched as DE MLO) is a vendor certification aimed at practitioners who build and operate machine learning workflows on the Databricks Lakehouse Platform. It suits data scientists, ML engineers, and MLOps engineers who already work with MLflow, Spark, and Delta Lake and want independent evidence that they can take a model from experimentation through deployment and ongoing monitoring. The DE MLO case studies are 5 cases and about 4,343 words.
1The Fraud Model Nobody Can Rebuild
A payments company runs about 400 training jobs a week on a shared Kubernetes cluster. Runs are logged to a self-hosted MLflow server with a PostgreSQL backend and S3 artifact storage.
882 words, about 4 min2Recall Collapse After a Feature-Store Swap
A card issuer's fraud team trains a gradient-boosted tree on 18 months of transactions, roughly 12 million per day with 0.42% fraud. Offline AUC is 0.93 and has been stable for a year.
859 words, about 4 min3Recommendation Serving at 340 ms p99
A retail marketplace serves product recommendations through a Python model server on 8 GPU nodes. The p99 latency SLO is 120 ms at 200 requests per second, and 70% of traffic is mobile with a 300 ms client timeout.
852 words, about 4 min4Forecast Drift With a Thirty-Day Label Lag
A utility company operates a day-ahead demand forecasting model for 40 substations, run once nightly against three years of history. At launch the fleet-level MAPE on the evaluation harness was 11.2%; over the last six weeks it has drifted to 19.4%.
870 words, about 4 min5Fourteen Months Later, the Auditor Asks
A bank uses a gradient-boosted credit-scoring model to approve small-business credit lines. A regulator's auditor asks the bank to reproduce the exact score and reason codes issued to a specific applicant 14 months ago.
880 words, about 4 min