“The Microsoft Fabric implementation program gave our data engineering team a structured path from legacy pipelines to a modern lakehouse architecture.”
Enterprise Program Brief
Role-Based Certification PrepTrack: Databricks Certified Machine Learning AssociateOfficial Source: Databricks
DatabricksIntermediate
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.
Duration
4 days
Level
Intermediate
Format
Virtual, On-site, or Hybrid
Language
English
Executive brief for stakeholdersDownload Program Brief (PDF)
In Demand
Databricks
Machine LearningML
Preparation, development, deployment, MLOps
Databricks ML
ML
On this page
Ideal for
Machine Learning EngineerMachine LearningCertification ReadinessTailored Team Delivery
Audience Profile
Built for these roles
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
Executive overview
Databricks machine learning practitioner program aligned to associate-level ML responsibilities across preparation, model building, deployment, and operations.
Readiness
Prerequisites
- Python experience for data and model workflows.
- Working knowledge of machine learning fundamentals.
- Basic exposure to notebooks or cloud-based analytics environments.
Program Outcomes
Capabilities your teams will gain
Prepare and manage data for machine learning workflows on Databricks
Develop and evaluate models using Databricks-native tooling
Deploy models into repeatable delivery flows
Establish stronger ML operations habits for team-based execution
Curriculum
Curriculum roadmap
1
Data Preparation for Machine Learning
2
Machine Learning Model Development
3
Machine Learning Model Deployment
4
Machine Learning Operations
1Module 1
Prepare and structure data for ML workloads
+
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
+
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
+
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
+
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
Delivery Models
Delivery models
Virtual ILTOnsiteHybridExecutive WorkshopBootcampWeekend
Engagement Fit
Engagement fit
Certification readinessImplementation-focused labsPrivate cohort deliveryIntermediate practitioner depth
Enterprise Customization
Enterprise customization
Tailor this program to your organization's priorities: Builds applied machine learning capability on Databricks across feature preparation, model development, deployment, and core MLOps operations.
- •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
Credentials
Certification & official source
- •Databricks Certified Machine Learning Associate
Aligned to the official Databricks training catalog and certification guidance for this program.
View Databricks Training SourceResources
Program resources
Yes. Most enterprise clients prefer private delivery scoped to role mix, timezone, and rollout timeline. We align lab environments and scenarios to your tenant context where applicable.
Enterprise Proof
Trusted delivery outcomes
“We needed a partner who understood both the technical depth of Azure OpenAI and the governance requirements of an enterprise.”
Delivery Capability
Enterprise-grade instruction
MCT-led delivery
Programs led by Microsoft Certified Trainer practitioners
Enterprise program oversight
Founder-led specialist delivery with structured rollout planning
Global delivery
APAC · EMEA · Americas · Virtual & Onsite
Implementation-focused
Hands-on labs aligned to production scenarios
