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Databricks ML-PRO Cheat Sheet: MLOps, Governance, and Serving

April 13, 2026

Databricks ML-PRO cheat sheet for MLOps, governance, serving, traps, and final review.

On this page

Use this for last-mile review. ML-PRO usually gets easier when you classify the stem first instead of treating every production issue as “a model problem.”

Fast lane picker

If the question is mainly about… Strongest first lane
SparkML pipeline design, estimators, or transformers chapter 1
distributed training, tuning, Spark vs Ray, or scaling strategy chapter 1
nested runs, feature lookup correctness, or online tables chapter 1
aliases, lifecycle stages, testing, or Asset Bundles chapter 2
automated retraining, drift metrics, or alerting chapter 2
blue-green, canary, or custom serving deployment chapter 3

ML-PRO answer sequence

Use this when the stem mixes training strategy, lifecycle management, monitoring, or deployment safety.

    flowchart TD
	  S["Scenario"] --> M["Classify the production ML problem"]
	  M --> T["Pick the training or inference path"]
	  T --> L["Check lifecycle, alias, or version behavior"]
	  L --> O["Check monitoring, drift, and deployment control"]
	  O --> V["Verify rollout safety and rollback path"]

Production answer rules

If you need to choose between… Better ML-PRO instinct
best offline score vs safest governed release safest governed release
retrain vs rollback decide whether the issue is drift or bad release first
more scale vs better workload fit fit the training or inference strategy before adding cost
generic MLOps pattern vs Databricks-native lifecycle control Databricks-native lifecycle control

Scaling and inference map

Requirement Better first instinct
massive feature matrix and distributed preprocessing SparkML
low-latency request-time scoring serving-oriented inference path
scheduled large scoring job batch inference
parallelization across large ML workloads decide between data parallelism, model parallelism, Spark, and Ray based on the real constraint

Lifecycle and monitoring map

Signal or need Better first action
need a stable pointer to the currently trusted version alias
need to compare a candidate against release history lifecycle and version control before deployment
detect gradual data or model-quality change Lakehouse Monitoring and drift metrics
detect serving health issues deployment and endpoint health lane, not just model metrics
decide whether to retrain automatically define trigger plus top-model selection logic first

High-confusion pairs

Pair Keep this distinction clear
MLflow run vs registered model version experiment record vs release artifact
offline metric gain vs safe promotion model score improvement vs governed release decision
SparkML vs single-node model distributed ML pipeline vs simpler local model path
Spark vs Ray different distributed training ecosystems and trade-offs
drift vs rollout regression gradual change vs bad release event
alias vs serving endpoint release pointer vs deployed interface

Last 15-minute recheck

Recheck this Because the miss often hides here
point-in-time correctness and feature reuse leakage and feature inconsistency cause many near-misses
run vs version vs alias lifecycle questions break here first
Spark vs Ray vs single-node fit scaling questions punish habit answers
test scope across dev, test, and prod ML systems need more than one type of validation
drift signal vs serving failure monitoring questions punish collapsed reasoning
rollout path and rollback path deployment questions reward blast-radius control

One-sentence memory hooks

  • If the score improved offline, ask whether the release is still safe.
  • If production got worse, ask whether the problem is drift, features, serving, or rollout before retraining.
  • If scaling is the question, choose fit before size.
  • If monitoring fires, tie it to a concrete action: retrain, rollback, block promotion, or fix upstream data.
Revised on Sunday, May 10, 2026
Study Plan
Sample Questions