🔷 Databricks ML Professional Exam Prep

An independent, free exam-preparation course that walks through the publicly published objectives of the Databricks Certified Machine Learning Professional exam. It covers the Lakehouse and Databricks Machine Learning platform, advanced experimentation and data management with Delta and the Feature Store, the full MLflow toolkit (Tracking, Projects, Models, Model Registry), AutoML, distributed training at scale with Spark ML, hyperparameter tuning with Hyperopt, the model lifecycle and Registry stages, batch, streaming and real-time inference, Model Serving, monitoring and data/concept drift, webhooks and CI/CD automation, and Unity Catalog governance — closing with deployment strategies and exam strategy. The official exam has 59 questions over 120 minutes across three domains (Model Development 44%, ML Ops 44%, Model Deployment 12%). This course uses visual lessons, original self-check questions and a 45-question final exam to build production-ready understanding. It is awareness and preparation only — not the official Databricks training or the certification exam — and claims no Databricks affiliation or endorsement. The course is organized into 22 modules, ending with a final exam (pass mark 80%). It is independent, free exam-preparation training — not an official or accredited review course.

What you'll learn

• The Lakehouse & Databricks ML overview
• Delta Lake for ML data management
• Feature Store & Feature Engineering
• MLflow Tracking: runs, params & metrics
• MLflow Models & flavors
• MLflow Projects & reproducibility
• The MLflow Model Registry
• Model lifecycle & Registry stages
• AutoML for baselines & glass-box code
• Training at scale with Spark ML
• Distributed training: pandas UDFs & beyond
• Hyperparameter tuning with Hyperopt
• Batch inference at scale
• Streaming inference with Structured Streaming
• Real-time Model Serving
• Monitoring & data drift detection
• Automated retraining workflows
• Model Registry webhooks
• CI/CD & Databricks Asset Bundles
• Unity Catalog governance for ML
• Deployment strategies & rollout
• Exam strategy & final review

Learning objectives

• Navigate the Databricks Lakehouse and Databricks Machine Learning workspace, clusters and the ML runtime
• Manage experimentation data with Delta tables and the Databricks Feature Store
• Track, version and reproduce experiments with MLflow Tracking, Projects and Models
• Operate the MLflow Model Registry and govern models through lifecycle stages and aliases
• Train models at scale with Spark ML and distributed training, and accelerate selection with AutoML
• Tune hyperparameters efficiently using Hyperopt and SparkTrials
• Deploy batch, streaming and real-time inference, including Databricks Model Serving
• Build monitoring solutions that detect data and concept drift and trigger retraining
• Automate the MLOps lifecycle with webhooks, jobs and CI/CD, governed by Unity Catalog
• Choose deployment and rollout strategies and apply exam strategy
• Pass a 45-question exam (80% to earn your certificate)