Infra & Admin10
DP-3028-A: Implement Generative AI engineering with Azure Databricks
Duration
1 Day
Upcoming Batch
20 October 2026
Batch Options
4 hours & 8 hours
Language
English / Arabic
Course Overview
The DP-3028-A: Implement Generative AI Engineering with Azure Databricks course is designed for data engineers, AI developers, and solution architects who aim to leverage the full potential of Generative AI within the Azure Databricks ecosystem. Participants will gain advanced expertise in designing, building, and deploying generative AI models and pipelines for enterprise-grade applications.
Through hands-on labs, real-world case studies, and advanced engineering techniques, learners will explore the integration of large language models (LLMs), natural language processing (NLP), and machine learning workflows within Databricks. The course emphasizes scalable, secure, and efficient AI solutions that drive business insights, automation, and innovation.
In this course, you will:
- Gain hands-on experience implementing Retrieval-Augmented Generation (RAG) and fine-tuning large language models (LLMs).
- Explore multi-stage reasoning techniques using LangChain, LlamaIndex, Haystack, and DSPy.
- Understand and apply LLMOps practices for model deployment, monitoring, and governance with MLflow and Unity Catalog.
- Incorporate responsible AI principles, including risk mitigation and ethical considerations.
- Build and operationalize generative AI solutions using Azure Databricks and Apache Spark.
- Acquire in-demand generative AI skills in a focused, one-day training format.
Prerequisites:
Before starting this module, you should be familiar with fundamental Azure Databricks concepts.
Target Audience
- Data Engineers: Professionals responsible for building and maintaining scalable data pipelines and preparing datasets for generative AI model training.
- AI / ML Developers: Developers working on implementing, fine-tuning, and deploying large language models (LLMs) and NLP-based solutions.
- Solution Architects: Individuals designing enterprise-level AI and analytics solutions who need to integrate generative AI into business workflows.
- Data Scientists: Professionals seeking to leverage Azure Databricks for advanced generative AI experimentation and model optimization.
- Azure Cloud Engineers: Cloud professionals managing Azure resources and services for AI deployment at scale.
- Enterprise AI Consultants: Consultants responsible for delivering AI-driven solutions and digital transformation strategies to clients.
Course Content
7 modules · 35 topics01Module 1: Get started with language models in Azure Databricks5 topics
- Understand Generative AI
- Understand Large Language Models (LLMs)
- Identify key components of LLM applications
- Use LLMs for Natural Language Processing (NLP) tasks
- Exercise – Explore language models
02Module 2: Implement Retrieval Augmented Generation (RAG) with Azure Databricks5 topics
- Explore the main concepts of a RAG workflow
- Prepare your data for RAG
- Find relevant data with vector search
- Rerank your retrieved results
- Exercise – Set up RAG
03Module 3: Implement multi-stage reasoning in Azure Databricks6 topics
- What are multi-stage reasoning systems?
- Explore LangChain
- Explore LlamaIndex
- Explore Haystack
- Explore the DSPy framework
- Exercise – Implement multi-stage reasoning with LangChain
04Module 4: Fine-tune language models with Azure Databricks4 topics
- What is fine-tuning?
- Prepare your data for fine-tuning
- Fine-tune an Azure OpenAI model
- Exercise – Fine-tune an Azure OpenAI model
05Module 5: Evaluate language models with Azure Databricks5 topics
- Explore LLM evaluation
- Evaluate LLMs and AI systems
- Evaluate LLMs with standard metrics
- Describe LLM-as-a-judge for evaluation
- Exercise – Evaluate an Azure OpenAI model
06Module 6: Review responsible AI principles for language models in Azure Databricks5 topics
- What is responsible AI?
- Identify risks
- Mitigate issues
- Use key security tooling to protect your AI systems
- Exercise – Implement responsible A
07Module 7: Implement LLMOps in Azure Databricks5 topics
- Transition from traditional MLOps to LLMOps
- Understand model deployments
- Describe MLflow deployment capabilities
- Use Unity Catalog to manage models
- Exercise – Implement LLMOps
Schedule Dates
4 upcoming batchesSession TypePhysical sessions run at our training facility.
| Batch Dates | Duration | Batch Options | Language | Action |
|---|---|---|---|---|
| 20 October 2026 | 1 Day | 4 hours & 8 hours | English / Arabic | |
| 20 January 2027 | 1 Day | 4 hours & 8 hours | English / Arabic | |
| 20 April 2027 | 1 Day | 4 hours & 8 hours | English / Arabic | |
| 20 July 2027 | 1 Day | 4 hours & 8 hours | English / Arabic |
Can’t find a suitable date? Request a schedule that fits your team.
Request More InformationFAQs
What is the primary focus of the DP-3028-A course?
This course is designed for data engineers and AI practitioners looking to build, deploy, and manage generative AI solutions. It specifically focuses on leveraging the Databricks Data Intelligence Platform to handle the full lifecycle of Large Language Models (LLMs), from data preparation to RAG (Retrieval-Augmented Generation) architectures.
Will this course cover RAG (Retrieval-Augmented Generation)?
Yes. A core pillar of DP-3028-A is implementing RAG workflows. You will learn how to:
- Chunk and vectorize proprietary data.
- Store data in Databricks Vector Search.
- Connect these vectors to LLMs to provide contextually accurate, company-specific responses.
Does the course include Model Evaluation (LLMops)
Absolutely. Building an AI is only half the battle; the course covers MLflow for LLMs, tracking experiments, and using Databricks Model Serving to deploy your models with low latency and high reliability.
How does this differ from a general Azure AI Engineer course (like AI-102)?
While AI-102 focuses on pre-built Azure AI Services (like Vision or Language APIs), DP-3028-A is an engineering-heavy deep dive into the Databricks ecosystem. It is tailored for those who need to build custom, scalable AI architectures rather than just consuming “off-the-shelf” APIs.
Is this course updated for the latest GenAI trends?
Yes. The curriculum is regularly updated to include the latest advancements in Mosaic AI and the integration of the latest models (like Llama 3 or GPT-4o) via Azure OpenAI.
How We Deliver
Flexible Training Options to Meet Your Needs
Choose how you learn — live online, in-classroom, at your workplace, or internationally. CounselTrain delivers certified IT training across the UAE in the format that fits your team.
Select the method that best suits your needs.
Online Instructor-Led Training
Learn from the comfort of your workplace or at home through live virtual sessions led by expert trainers.
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Classroom Training
Participate in interactive, face-to-face training in our top 5-star training facilities in Dubai.
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Onsite Training
Learn a customised curriculum in your workplace to ensure the most impact and team participation.
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Overseas Training
Participate in our international training sessions and improve your abilities with world-class instructors.
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