Model development on the Databricks lakehouse
Databricks' Machine Learning Associate focuses on AutoML, feature engineering, MLflow, and model training, a data-platform route for practitioners who build models alongside their data pipelines.
Who it suits
Fits practitioners who develop models on Databricks or want a first ML credential on that platform. Engineers expecting to architect production ML systems on a cloud may want broader scope.
Key differences
- It is tied to Databricks tooling such as MLflow rather than a hyperscaler's managed ML services.
- Model development and evaluation are the core, so production operation and monitoring are treated more lightly than in the Google exam.
- It targets foundational capability at associate level, whereas the Google credential assumes professional-level design experience.
- Level
- Associate
- Assessment
- Written
- Study time
- 50-90h
- Listed from
- $200