Databricks Certified Machine Learning Professional

The Databricks Certified Machine Learning Professional is the advanced-level ML certification for practitioners who design and operate production ML systems on the Databricks Lakehouse Platform. It validates expertise in MLOps, model governance, advanced feature engineering, and scalable ML pipeline architecture using MLflow, Feature Store, and Databricks Model Serving.

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What is the Databricks Certified Machine Learning Professional?

Who Should Take This Course?

Senior ML Engineers managing production ML systems
MLOps practitioners building model deployment pipelines
Data Scientists operationalizing ML at enterprise scale
Platform architects designing ML infrastructure on Databricks
Technical leads overseeing end-to-end ML lifecycle

What You Will Learn in the MLP Course

A comprehensive curriculum covering all exam objectives with hands-on labs and real-world practice.

Advanced MLflow and Experiment Management

Master MLflow at scale for enterprise ML workflows.

Custom MLflow flavors and logging strategies
Multi-step MLflow workflows and nested runs
Model Registry automation and webhooks
MLflow Projects for reproducible pipelines

Feature Store and Feature Engineering at Scale

Design and manage enterprise feature stores.

Databricks Feature Store architecture
Point-in-time feature lookup for training/serving
Feature freshness and serving optimization
Cross-team feature sharing and governance

Advanced Model Training and Tuning

Scale model training across distributed clusters.

Distributed deep learning with Horovod and Torch Distributor
Advanced hyperparameter optimization strategies
Ensemble methods and model stacking
AutoML customization and constraints

MLOps and Production Deployment

Build robust pipelines for production ML deployment.

CI/CD for ML: testing, staging, and production promotion
Model serving with real-time and batch endpoints
A/B testing and shadow deployment strategies
Rollback and canary releases for models

Model Monitoring and Governance

Monitor deployed models and ensure responsible AI.

Data drift and concept drift detection
Model performance monitoring dashboards
Fairness, bias, and explainability (SHAP, LIME)
Audit trails and lineage with Unity Catalog

Course Prerequisites

Pre-requisites training is free when you purchase the course from ProSupport

Databricks Certified Machine Learning Associate or equivalent
2+ years of ML engineering or data science experience
Strong Python, PySpark, and ML framework proficiency
Experience deploying ML models to production

Exam Information

Everything you need to know about the MLP certification exam.

Exam Component	Details
Exam Name	Databricks Certified Machine Learning Professional
Exam Code	MLP
Exam Type	Multiple Choice + Short Answer
Total Questions	60
Passing Score	70%
Exam Duration	120 minutes
Language	English
Exam Provider	Databricks / Kryterion
Exam Focus	Production ML, MLOps, Feature Store, model monitoring, and distributed training on Databricks
Exam Registration	Databricks Academy portal (academy.databricks.com)
Retake Policy	14-day waiting period before retake
Certification Validity	2 years