Your Databricks Machine Learning Associate Study Roadmap

Start with the workspace concepts learners use daily: notebooks, clusters or compute, data access, collaboration, and saved artifacts. Understand where code runs, where data is read, and how notebooks fit into ML development. This creates the context for every later workflow topic.

Review notebooks as ordered workflows. Identify where data is loaded, transformed, split, trained, evaluated, and logged. Practice reading a notebook and explaining the purpose of each cell. A notebook is more than code snippets; it is often the visible record of the ML development process.

Use Spark concepts to prepare data for ML. Review joins, filters, aggregations, missing-value handling, train-test splits, and transformations. Focus on how data preparation affects model quality and how mistakes can create leakage, inconsistent features, or unreliable evaluation.

Study feature creation, categorical handling, derived features, feature reuse, and training-serving consistency. Connect feature engineering to both model performance and deployment. A feature that works during training but cannot be produced during inference creates an operational problem.

Practice reading experiments and runs. Know parameters, metrics, artifacts, logged models, and comparison views. MLflow study should answer practical questions: which run performed best, what changed between runs, where are artifacts stored, and how can another team member reproduce the work?

Study AutoML as a way to generate candidate models and baseline notebooks. Then review evaluation metrics and limitations. Candidates should be able to interpret AutoML output rather than assuming the generated result is automatically production-ready.

Review batch inference, online serving, model endpoints, feature serving, and the operational concerns around serving a model. Ask what latency is needed, how features are retrieved, how model versions are managed, and how the team would detect or respond to a bad deployment.

Study governance after the workflow is clear. Unity Catalog concepts matter because data, features, and models need permissions, lineage, and discoverability. Governance questions often test how teams safely collaborate with shared ML assets.

Finish with mixed review across notebooks, Spark preparation, feature engineering, MLflow, AutoML, registry, Unity Catalog, feature serving, and deployment. Sort missed questions by workflow step and repeat the weakest step before taking another broad set.