Advanced Machine Learning with Databricks
Aligned to the Databricks Certified Machine Learning Professional track, this program focuses on designing and operating enterprise-scale machine learning systems on Databricks. It emphasizes scalable model pipelines, advanced MLflow usage, testing, automated retraining, environment management, and deployment strategies for high-confidence production ML.
Certification
Databricks Certified Machine Learning Professional
Delivery
Virtual, On-site, or Hybrid
Duration
2 days
Product
Databricks Data Intelligence Platform
Role
Machine Learning Engineer
Databricks
Advanced MLOpsScale, serving, monitoring, lifecycle
Databricks ML
Program Guide
Best Fit
Audience Profile
Who This Program Is For
Designed for experienced machine learning practitioners who need to implement scalable, monitored, production-grade ML systems using advanced Databricks platform capabilities.
Overview
Program Summary
Advanced Databricks ML program aligned to professional-level machine learning engineering across scale, automation, environment management, and production 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
Scale enterprise machine learning workloads
+
Module 1
Scale enterprise machine learning workloads
Design training and inference workflows that take advantage of Databricks for larger-scale machine learning pipelines, distributed processing, and performance-aware execution.
- Machine Learning at Scale
2Module 2
Operate advanced MLOps workflows
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Module 2
Operate advanced MLOps workflows
Apply stronger practices for model lifecycle control, testing, deployment automation, monitoring, and reliable production operations.
- Advanced Machine Learning Operations
Coverage Areas
Topic Coverage
Coverage Item 1
Machine Learning at Scale
Coverage Item 2
Advanced 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.
- •Focus on model serving and release management for production teams
- •Add deeper retraining and monitoring scenarios relevant to your MLOps setup
- •Extend with Databricks Asset Bundles and environment promotion patterns
