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Training: Implement Generative AI engineering with Azure Databricks (DP-3028)

Ref. DP-3028
Duration:
1
 jour
Exam:
Not certifying
Level:
Intermédiaire

Implement Generative AI engineering with Azure Databricks (DP-3028)

AI engineering is no longer reserved for large research teams. With Azure Databricks, professionals can now design, test, and deploy generative AI solutions at scale. This course guides you step-by-step through the implementation of powerful language models, while integrating advanced techniques such as Retrieval-Augmented Generation (RAG) and custom model optimization.

A hands-on approach to generative AI engineering

In practical terms, you’ll learn how to work with AI models, adapt them to your business data, and evaluate their performance. Each training module is designed to combine theory with hands-on practice. You’ll build complete and efficient workflows, leveraging the collaborative and scalable environment of Azure Databricks.

Participant Profiles

  • Data scientists
  • AI engineers
  • Machine learning developers
  • Cloud solution architects

Objectives

  • Implement generative language models in Azure Databricks
  • Design RAG workflows to enhance LLM responses
  • Fine-tune and evaluate the performance of Azure OpenAI models
  • Apply responsible AI principles in your deployments
  • Deploy and orchestrate models using LLMOps

Prerequisites

  • Understand the basics of artificial intelligence and machine learning
  • Master the fundamentals of Azure Databricks
  • Have some knowledge of natural language processing (NLP)

Course Content

Module 1: Getting started with language models in Azure Databricks

  • Understand generative AI
  • Understand large language models (LLMs)
  • Identify the key components of LLM applications
  • Use LLMs for natural language processing (NLP) tasks

Module 2: Implementing Retrieval-Augmented Generation (RAG) with Azure Databricks

  • Explore the main concepts of a RAG workflow
  • Prepare your data for RAG
  • Retrieve relevant data using vector search
  • Rerank your retrieved results

Module 3: Implementing multi-step reasoning in Azure Databricks

  • What are multi-step reasoning systems?
  • Explore LangChain
  • Discover LlamaIndex
  • Discover Haystack
  • Explore the DSPy framework

Module 4: Fine-tuning language models with Azure Databricks

  • What is fine-tuning?
  • Prepare your data for optimization
  • Fine-tune an Azure OpenAI model

Module 5: Evaluating language models with Azure Databricks

  • Compare LLM and traditional ML evaluation
  • Evaluate LLMs and AI systems
  • Evaluate LLMs using standard metrics
  • Describe LLM-as-a-judge for evaluation

Module 6: Reviewing responsible AI principles for language models in Azure Databricks

  • What is responsible AI?
  • Identify risks
  • Mitigate issues
  • Use key safety tools to protect your AI systems

Module 7: Implementing LLMOps in Azure Databricks

  • Transition from traditional MLOps to LLMOps
  • Understand deployment patterns
  • Describe MLflow deployment capabilities
  • Use Unity Catalog to manage models

Documentation

  • Access to Microsoft Learn, Microsoft’s online learning platform, offering interactive resources and educational content to deepen your knowledge and develop your technical skills.

Lab / Exercises

  • This course provides you with exclusive access to the official Microsoft lab, enabling you to practice your skills in a professional environment.

Complementary Courses

Additional Information

Unlock the full potential of generative models in Azure

Large language models are deeply transforming how we work in analytics, data science, and artificial intelligence. With Azure Databricks, these technologies are now available in a powerful and collaborative cloud environment. This training offers a hands-on, structured approach to understanding and implementing generative models in real-world use cases.

Unlike a simple introduction to AI, this program is designed for professionals who want to go further. It enables you to master not only the models themselves, but also the entire surrounding ecosystem: data management, performance evaluation, optimization, security, governance, and deployment. You will learn to think like a modern AI engineer, capable of integrating complex components into a robust pipeline.

Build AI solutions focused on quality and responsibility

Today, building a high-performing model is no longer enough. It also needs to be interpretable, reliable, and ethically compliant. That’s why part of the program is dedicated to responsible AI. You’ll learn how to identify risks, integrate security tools, and design models aligned with industry best practices. Azure Databricks provides key features to help you implement these standards.

You’ll also discover how to structure your data to improve the relevance of your models. The fine-tuning approach allows you to adapt an existing model to a specific business case without having to rebuild it from scratch. This strategy saves time, reduces resource usage, and delivers much more accurate results.

An architecture built for scalability and deployment

AI engineering doesn’t stop at model training. You also need to think about production. That’s why the training includes a complete module dedicated to LLMOps. You’ll learn how to deploy your models in Azure using MLflow, track their performance over time, and automate updates. Unity Catalog will help you centralize the management of models, access rights, and resources.

One of Azure Databricks’ key strengths is its ability to combine advanced data processing tools with cutting-edge AI models. By learning to leverage this synergy, you’ll be able to create intelligent solutions that connect to your data sources and integrate seamlessly with your cloud infrastructure.

A hands-on training program for rapid upskilling

This program is structured to help participants quickly build their skills. Each topic covers a key area of modern AI engineering. You’ll discover tools like LangChain, LlamaIndex, and Haystack, which allow for complex reasoning workflows. You’ll also learn to objectively evaluate your models using appropriate metrics and approaches such as LLM-as-a-judge.

Thanks to practical exercises, you’ll know how to apply the concepts to your internal projects. The goal isn’t just to understand, but to build. And above all, to replicate these methods in various contexts while meeting quality, ethical, and scalability requirements.

FAQ

Is this training accessible to non-developers?
Yes. If you already have a basic understanding of AI and data handling, you can follow the program even without being an experienced developer.

How is this course different from a standard Azure course?
This course focuses on generative AI and language models. It goes far beyond simply using Azure. It teaches you how to build, fine-tune, secure, and deploy advanced models.

Can I apply these skills in my company?
Absolutely. The content is designed for real-world application, with tools, techniques, and workflows that can be directly used in enterprise AI projects — whether in development, research, or production.

Prix de l'inscription
CHF 850.-
Inclus dans ce cours
  • Training provided by a certified trainer
  • 180 days of access to Official Microsoft Labs
  • Official documentation in digital format
  • Official Microsoft achievement badge
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Contact

ITTA
Route des jeunes 35
1227 Carouge, Suisse

Opening hours

Monday to Friday
8:30 AM to 6:00 PM
Tel. 058 307 73 00

Contact-us

ITTA
Route des jeunes 35
1227 Carouge, Suisse

Make a request

Contact

ITTA
Route des jeunes 35
1227 Carouge, Suisse

Opening hours

Monday to Friday, from 8:30 am to 06:00 pm.

Contact us

Your request