Databricks Certified Machine Learning Professional

Databricks ML Professional · ● Active · Professional · Databricks

Note: This certification validates advanced expertise in building, deploying, and monitoring enterprise-scale machine learning solutions on the Databricks Lakehouse platform. Every URL verified as of May 2026.

Exam facts

Vendor source — Databricks Certified Machine Learning Professional ↗ Official exam guide — ML Professional Exam Guide ↗ Exam registration — Webassessor Registration ↗

About

The Databricks Certified Machine Learning Professional certification validates expertise in designing, implementing, and managing enterprise-scale machine learning solutions using advanced Databricks platform capabilities. Released in 2023, it assesses proficiency across the full ML lifecycle: experimentation, model lifecycle management, deployment, and production monitoring. This professional-level credential is designed for ML engineers and data scientists with 1+ years of hands-on experience building production ML systems at scale. It focuses on practical implementation skills in SparkML, MLflow, Feature Store, Model Serving, and Lakehouse Monitoring within the Databricks Lakehouse architecture.

Domain context — Data & AI

Advanced machine learning engineering and MLOps in the modern lakehouse platform ecosystem, bridging traditional ML practices with unified data and AI infrastructure.

Read full deep dive — Databricks Lakehouse Ecosystem → (file not yet created)

Topics covered

The exam blueprint covers four weighted domains totaling 100%:

• Experimentation (30%) — Experiment tracking with MLflow, hyperparameter tuning with Ray/Optuna, distributed training, parameter logging, artifact management, and run organization
• Model Lifecycle Management (30%) — MLflow Model Registry, model versioning, stage transitions (dev → staging → production), model metadata, lineage tracking, and governance
• Model Deployment (25%) — Deployment strategies (blue-green, canary), custom model serving with PyFunc, REST API integration, Unity Catalog for model registration, and rollout management
• Solution and Data Monitoring (15%) — Lakehouse Monitoring for drift detection, model performance tracking, data quality validation, schema evolution, and production alerts

Source: Official Databricks Certification FAQ ↗

Common skills at Data & AI · Professional

Shared expertise for professional-level machine learning engineering and MLOps.

• Distributed model training and hyperparameter optimization at scale
• Production ML pipeline orchestration and automation
• Model governance, versioning, and registry management
• Real-time feature engineering and serving
• Production monitoring and drift detection
• Infrastructure-as-code for ML asset deployment (Databricks Asset Bundles)

Recommended courses at Data & AI · Professional

Course-selection rule: Databricks Academy courses are the official path; supplement with Udemy hands-on labs for scenario-based practice. Focus on MLflow, Feature Store, and Model Serving specifics rather than foundational ML.

Practice exams

Books

Book rule: No single study guide exists specifically for the Databricks ML Professional exam. MLflow and ML systems design books provide foundational context; supplement with official Databricks documentation and academy courses for platform-specific topics.

Typical job titles at Data & AI · Professional

Machine Learning Engineer · Senior ML Engineer · MLOps Engineer · ML Architect · Machine Learning Operations Specialist · Data Scientist (advanced)

(Job titles drawn from Databricks job board postings and Glassdoor entries listing this certification as required or preferred.)

Salary

Salary note: Databricks ML Engineer median total compensation at L5–L6 ranges from $250K–$350K including equity. Certification-specific salary premia of +$15K–$30K reported in professional surveys. Entry ML Engineer salaries without Databricks experience typically start at $100K–$130K USD.

Skills validated

Specific technologies and practices tested in this exam.

• MLflow — Experiment tracking, model logging, runs, artifacts, model registry, stage transitions, model flavors, PyFunc
• Distributed Training — Ray Tune, Optuna, hyperparameter optimization at scale, distributed SparkML pipelines
• Feature Store — Databricks Feature Store, point-in-time correctness, automated feature pipelines, online tables, feature engineering client
• Model Serving — Custom model serving, PyFunc, REST API endpoints, deployment strategies (blue-green, canary), rollout management
• Lakehouse Monitoring — Drift detection, data quality validation, schema evolution tracking, performance metrics, production monitoring
• Databricks Asset Bundles — Infrastructure-as-code for ML, YAML configuration, environment management, reproducible deployments
• Unity Catalog — Model governance, access control, lineage, cross-workspace model sharing
• Advanced MLOps — CI/CD for ML, testing (unit & integration), automated retraining, monitoring pipelines

Related certifications

• Foundation for this level: Databricks Certified Machine Learning Associate ↗ (prerequisite experience; not required)
• Stacks with: Databricks Certified Data Engineer Professional ↗ (complementary platform expertise)
• Advanced alternative: Databricks Advanced Certifications (LLM Engineering, Generative AI) — coming 2026
• Vendor overview: Databricks Vendor Overview ↗ (file not yet created)

Sources

• Databricks Certified Machine Learning Professional
• Databricks Certification & Badging FAQ
• Webassessor Exam Registration
• Databricks Academy Training Catalog
• Glassdoor Databricks Salaries
• Levels.fyi Databricks Compensation Data
• PayScale Machine Learning Engineer Salary ZA
• Learning MLflow — O'Reilly
• Designing Machine Learning Systems — O'Reilly
• Databricks Academy: Machine Learning with Databricks
• ExamTopics Databricks ML Professional Practice
• CertFun ML Professional Guide

Last verified: 2026-05-01 Parent ecosystem: Databricks Lakehouse Ecosystem Parent domain: Data & AI Vendor overview: Databricks Overview