Study Databricks ML-PRO Model Development: key concepts, common traps, and exam decision cues.
This is one of the two heavy ML-PRO domains. Databricks is testing whether you can scale model-development decisions without breaking feature consistency, experiment structure, or inference fit.
| Lesson | Focus |
|---|---|
| 1.1 SparkML Pipelines, Estimators and Transformers | Learn how ML-PRO frames scalable SparkML pipeline construction. |
| 1.2 Inference Fit, Single-Node vs SparkML, and Scoring Modes | Learn how the exam chooses inference and training fit based on workload shape. |
| 1.3 Distributed Training, Parallelization, Spark vs Ray | Learn how ML-PRO frames scaling strategy for large ML workloads. |
| 1.4 Distributed Hyperparameter Tuning with Optuna, Ray and MLflow | Learn how Databricks expects you to reason about tuning workflow and logging. |
| 1.5 Nested Runs, Point-in-Time Correctness and Online Features | Learn how advanced MLflow tracking and feature-engineering consistency fit together. |
| If the question is really about… | Go first to… |
|---|---|
| SparkML pipelines, transformers, or estimators | 1.1 SparkML Pipelines, Estimators and Transformers |
| batch vs streaming vs real-time inference, or single-node vs distributed fit | 1.2 Inference Fit, Single-Node vs SparkML, and Scoring Modes |
| Spark vs Ray, scaling, or parallelization strategy | 1.3 Distributed Training, Parallelization, Spark vs Ray |
| Optuna, Ray, MLflow callbacks, or distributed tuning | 1.4 Distributed Hyperparameter Tuning with Optuna, Ray and MLflow |
| nested runs, leakage prevention, or online feature workflow | 1.5 Nested Runs, Point-in-Time Correctness and Online Features |