Program Outline
Machine Learning with Databricks
This course maps to the Databricks machine learning practitioner learning path and supports Databricks Certified Machine Learning Associate preparation. It equips teams to move from experimentation into structured Databricks machine learning workflows, covering data preparation, model development, deployment, and foundational operations.
Certification
Databricks Certified Machine Learning Associate
Delivery
Virtual, On-site, or Hybrid
Duration
4 days
Product
Databricks Data Intelligence Platform
Role
Machine Learning Engineer
Databricks
Machine LearningPreparation, development, deployment, MLOps
Databricks ML
Best Fit
Audience Profile
Who This Program Is For
Built for machine learning practitioners who need to build, track, deploy, and operationalize ML workflows on Databricks with stronger engineering discipline and platform fluency.
Overview
Program Summary
Databricks machine learning practitioner program aligned to associate-level ML responsibilities across preparation, model building, deployment, and operations.
Course Outline
Complete Module Sequence
Review the full module sequence for this program, including the primary topic coverage in each module where available.
1Module 1
Prepare and structure data for ML workloads
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Module 1
Prepare and structure data for ML workloads
Create reliable machine learning-ready datasets and feature pipelines that support repeatable experimentation and downstream model development.
- Data Preparation for Machine Learning
2Module 2
Develop and evaluate machine learning models
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Module 2
Develop and evaluate machine learning models
Build model-development workflows in Databricks with a focus on training, evaluation, experimentation, and reproducibility.
- Machine Learning Model Development
3Module 3
Deploy models for enterprise use
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Module 3
Deploy models for enterprise use
Move from experimentation into usable delivery patterns by packaging, promoting, and exposing models in operational workflows.
- Machine Learning Model Deployment
4Module 4
Establish foundational ML operations
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Module 4
Establish foundational ML operations
Apply the operational practices needed to manage model lifecycle steps more consistently across collaboration, monitoring, and maintenance.
- Machine Learning Operations
Coverage Areas
Topic Coverage
Coverage Item 1
Data Preparation for Machine Learning
Coverage Item 2
Machine Learning Model Development
Coverage Item 3
Machine Learning Model Deployment
Coverage Item 4
Machine Learning Operations
Customization
Adapt This Program for Your Team
We can adapt this program around your team structure, platform priorities, delivery goals, and the scenarios your people need to work through in practice.
- •Use your data science use case or model type in the labs
- •Add deeper MLOps and model governance emphasis
- •Extend with feature engineering and serving patterns for production teams
