“We needed a partner who understood both the technical depth of Azure OpenAI and the governance requirements of an enterprise.”
Microsoft Official Curriculum
Role-Based Certification PrepTrack: DP-3028Official Source: Microsoft Learn
MicrosoftIntermediate
Implement Generative AI engineering with Azure Databricks
This course covers generative AI engineering on Azure Databricks, using Spark to explore, fine-tune, evaluate, and integrate advanced language models. It teaches how to implement techniques like retrieval-augmented generation (RAG) and multi-stage reasoning, as well as how to fine-tune large language models for specific tasks and evaluate their performance. Learners will also explore responsible AI practices for deploying AI solutions and how to manage models in production using LLMOps (Large Language Model Operations) on Azure Databricks.
Duration
1 day
Level
Intermediate
Format
Virtual, On-site, or Hybrid
Language
English
Executive brief for stakeholdersDownload Program Brief (PDF)
Microsoft
DataIT
Implement Generative AI engineering with Azure Databricks
Azure
Data
On this page
Ideal for
AI EngineerDataCertification ReadinessTailored Team Delivery
Audience Profile
Built for these roles
This course is designed for data scientists, machine learning engineers, and other AI practitioners who want to build generative AI applications using Azure Databricks. It is intended for professionals familiar with fundamental AI concepts and the Azure Databricks platform.
Overview
Executive overview
Official Microsoft Learn-aligned instructor-led program for Implement Generative AI engineering with Azure Databricks.
Readiness
Prerequisites
- Relevant foundational experience in the target technology area.
- Comfort with hands-on labs in a cloud or GPU-accelerated environment.
Program Outcomes
Capabilities your teams will gain
Strengthen capability in data scenarios
Strengthen capability in azure scenarios
Strengthen capability in role-based scenarios
Curriculum
Curriculum roadmap
1
Data
2
Azure
3
Role-Based
1Module 1
Implement Generative AI engineering with Azure Databricks
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Module 1
Implement Generative AI engineering with Azure Databricks
Generative Artificial Intelligence (AI) engineering with Azure Databricks uses the platform's capabilities to explore, fine-tune, evaluate, and integrate advanced language models. By using Apache Spark's scalability and Azure Databricks' collaborative environment, you can design complex AI systems.
- Get started with language models in Azure Databricks
- Implement Retrieval Augmented Generation (RAG) with Azure Databricks
- Implement multi-stage reasoning in Azure Databricks
- Fine-tune language models with Azure Databricks
- Evaluate language models with Azure Databricks
- Review responsible AI principles for language models in Azure Databricks
- Implement LLMOps in Azure Databricks
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 current Microsoft credential readiness for Implement Generative AI engineering with Azure Databricks using the official Microsoft Learn outline.
- •Align labs to your production environment and platform priorities
- •Add readiness reviews and instructor-led practice sessions
- •Extend into project-specific architecture or delivery coaching
Credentials
Certification & official source
- •DP-3028
Aligned to the official Microsoft Learn course and learning path for this program.
View Official Microsoft Learn PageResources
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
Banking & Finance
Representative Enterprise Banking Team
The focus was not just on tooling knowledge, but on helping teams work from a shared operating model as they adopted a more modern data platform.
- Clearer platform operating model across teams
- Improved confidence in modern data stack adoption
Healthcare
Representative Healthcare Product Team
The engagement helped product and engineering stakeholders move from interest in AI to clearer implementation choices, security expectations, and prototyping discipline.
- Stronger alignment between product and engineering teams
- Improved clarity on prototype-to-production requirements
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