Databricks Machine Learning Quickstart

Overview

85% of ML models never reach production—but yours will. This Short Course was created to help Machine Learning and Artificial Intelligence professionals accomplish rapid ML deployment using Databricks enterprise workflows. By completing this course, you'll be able to track experiments with MLflow, leverage AutoML to accelerate model development, and deploy serving endpoints with production-grade performance monitoring—skills you can apply immediately to your data pipelines. By the end of this course, you will be able to: ● Apply MLflow tracking to log runs, metrics, and artifacts for a baseline and AutoML-generated model within a Databricks workspace (Apply) ● Analyze AutoML experiment results to select a candidate model based on accuracy, runtime, and feature importance reports (Analyze) ● Evaluate model-serving endpoint performance and access controls to confirm readiness for production deployment (Evaluate) This course is unique because it provides hands-on experience with Databricks' unified platform, combining experiment tracking, automated machine learning, and model serving in a single integrated workflow that mirrors real enterprise deployment patterns.

Syllabus

MODULE 1: MLflow Tracking & Experiment Logging

High Level Description: Establish systematic experiment tracking using MLflow to create auditable ML workflows for regulatory compliance.

MODULE 2: AutoML Analysis & Model Selection

High Level Description: Leverage AutoML capabilities to accelerate model development while maintaining rigorous evaluation standards for credit risk assessment.

MODULE 3: Model Deployment & Endpoint Evaluation

High Level Description: Complete the ML workflow by deploying production-ready serving endpoints with comprehensive performance validation and access controls.