End-to-End Data & AI: Microsoft Fabric + Azure OpenAI

Name: End-to-End Data & AI: Microsoft Fabric + Azure OpenAI
Rating: 4.4 (58 reviews)

Build AI, ML and GenAI Use Cases with Microsoft Fabric | Work with SparkML and Azure Cognitive Services in Fabric

Created byKuljot Singh Bakshi

Last updated 4/2026

English

What you'll learn

Microsoft Fabric
Data Analytics
AI/ML with Microsoft Fabric
Generative AI (Azure OpenAI) with Microsoft Fabric

Course content

7 sections • 44 lectures • 9h 49m total length

Course Introduction4:48
Explore end-to-end data and AI with Microsoft Fabric and Azure OpenAI, unifying data lake house, one lake, and one leg for ETL, analytics, ML, and enterprise chat.

Data Analytical Engines in Fabric20:49
Explore data analytical engines in Microsoft Fabric, including Apache Spark and SQL analytics endpoint, and learn how startup pools and custom pools scale workloads in the lake house.
Getting Started with Fabric Portal7:59
Lab: Data Transformation with Apache Spark, SQL and T-SQL (Hands-On Lab)31:32
Lab: Default Semantic Model in Lakehouse (Hands-On Lab)5:52
What is Data Lakehouse and OneLake?25:03
OneLake Explorer2:17
Why Data Warehouse matters?4:33
Lab: Loading Data from a Lakehouse to a Warehouse (Hands-On Lab)12:50
Introduction to Semantic Models6:33
Explore semantic models in Microsoft Fabric and Power BI, learn how data lake house and delta tables form a data warehouse, and map facts to dimensions to build dashboards.
Lab: Creating a Facts and Dimensions Semantic Model (Hands-On Lab)17:16
Introduction to medallion architecture6:39
Lab: Implementing Medallion Architecture (Hands-On Lab)23:46
Introduction to Dataflows and Fabric Pipelines7:36
Lab: Building Dataflow and Pipeline (Hands-On Lab)17:41

Introduction to building your own custom models with Fabric8:44
Understanding Basic ML Concepts13:28
Explore basic machine learning concepts on Azure Databricks, including features and target columns, train-test splits, mean squared error, and the bias-variance trade-off to avoid overfitting and underfitting.
Understanding Linear Algorithms20:10
Understanding Non-Linear Algorithms15:49
Lab: Building Model for Diabetes Prediction (Hands-On Lab)29:09
Lab: Using Diabetes Predictor With Fabric Pipelines (Hands-On Lab)10:58
Lab: Model and Metrics Logging using MLflow (Hands-On Lab)9:19
Lab: Hyperparameter Tuning using Hyperopt and MLflow (Hands-On Lab)11:11
Explore hyperparameter tuning in a Microsoft Fabric workspace using the Hyperopt library to optimize a decision tree classifier with the diabetes training dataset, leveraging a lake house, notebook, and pipeline.
Introduction to Deep Learning Models15:55
Lab: Building Deep Learning Model with Fabric (Hands-On Lab)20:26
Build a deep learning diabetes predictor in a Fabric lake house using PyTorch, notebook workflows, 70/30 data splits, and model inference.

Introduction to Azure AI and Microsoft Fabric10:35
Project Overview: Sentiment Analysis for an E-Commerce Platform5:21
Develop an end-to-end sentiment analysis workflow for e-commerce platform by integrating Azure Language Services with Microsoft Fabric and building Power BI insights from a GitHub dataset and lakehouse semantic model.
Lab: Getting Comfy with Azure Language Service (Hands-On Lab)8:37
Lab: Building Sentiment Analysis Workflow in Fabric (Hands-On Lab)29:20

GenAI Primers and Foundations7:12
Explore the evolution from artificial intelligence to generative AI and large language models, and learn how transformer-based foundation models power applications like chatbots and Copilot.
Introduction to Azure OpenAI6:10
Discover how Azure OpenAI delivers tenant-isolated, secure access to OpenAI models like GPT and text embedding models through private endpoints and the Azure OpenAI Studio for deployment.
Introduction to Prompt Engineering6:06
Master prompt engineering by crafting precise prompts that define goal, context, expectations, and sources to guide AI agents. Narrow focus yields accurate, meaningful results across language, code, and images.
Lab: Getting Started with Azure OpenAI (Hands-On Lab)15:23
Lab: ChatCompletions API to call GPT Engine via Code (Hands-On Lab)13:06
What are Vector Embeddings?16:23
Vector embeddings convert words, phrases, and images into high-dimensional vectors such as Ada 002's 1536 dimensions, capturing semantic meaning to support retrieval augmented generation with Azure OpenAI.
Lab: Making use of Vector Embeddings via Code (Hands-On Lab)7:48

Introduction to GenAI and Fabric8:05
Lab: Creating a very Basic Chatbot with Fabric Notebooks (Hands-On Lab)7:13
Build a hands-on chatbot lab in fabric notebooks that chats with a GPT model hosted in Azure OpenAI, installing the OpenAI SDK and handling root certificates with certify.
What is RAG - Using Your Own Data16:28
Introduction to RAG with Microsoft Fabric15:01
Lab: Deploying Resources in Azure (Hands-On Lab)9:16
Lab: RAG Implementation in Fabric (Hands-On Lab)32:53
Deploy a RAG workflow in Fabric by building a lake house, uploading data, creating embeddings with SynapseML and Azure OpenAI, and indexing them in Azure Search for retrieval augmented generation.

Requirements

Programming Experience Required

Description

Unlock the Future of Unified Data & AI with Microsoft Fabric and Azure OpenAI!
Are you ready to become a certified expert in modern data analytics and artificial intelligence?
This comprehensive course, “Data and AI with Microsoft Fabric & Azure OpenAI,” equips you with the in-demand skills for mastering Microsoft’s unified analytics platform.

Designed for data engineers, data scientists, cloud professionals, and business analysts, this course provides hands-on training in Fabric’s complete suite — including Data Engineering, Lakehouses, Data Warehousing, Real-Time Analytics, ML with Synapse, and AI with Azure OpenAI.

What You’ll Learn:

Understand Microsoft Fabric Architecture: SaaS-based unified analytics platform covering data ingestion to visualization.
Master OneLake: Learn how Microsoft’s unified data lake enables secure, scalable, and collaborative data storage.
Data Engineering with Apache Spark and PySpark: Build ETL pipelines and automate workflows.
AI & ML Integration: Train and deploy ML models using MLflow, SynapseML, and Azure OpenAI for generative AI use cases.
Certification Ready: Aligns with objectives from DP-700 (Data Analyst Associate) and DP-600 (Fabric Analytics Engineer).
Real-World Projects: Work on practical scenarios simulating enterprise-scale data and AI implementations.

Who This Course is For:

Data Engineers & Scientists: Automate pipelines, manage big data, and deploy AI/ML solutions.
DP-700 / DP-600 Aspirants: Get certified with practical and theory-aligned content.
Cloud & AI Enthusiasts: Learn how Microsoft’s next-gen platform integrates with Azure OpenAI, Power BI, and Synapse.
Business Professionals & Leaders: Learn how to harness unified data platforms to drive insights and decision-making.

Tools and Technologies Covered:

Microsoft Fabric, OneLake, Data Factory, Data Engineering
Apache Spark, PySpark, MLflow, SynapseML
Power BI, Azure OpenAI, Lakehouses, Notebooks
Data Warehousing, Real-Time Analytics, Delta Lake

By the end of this course, you will:

Confidently navigate Microsoft Fabric’s end-to-end data workflow
Build and deploy AI models using Azure OpenAI and Synapse ML
Prepare for DP-700 and DP-600 certifications
Apply your skills to real-world projects in data engineering, analytics, and AI

Don’t miss this opportunity!

Stay ahead in the data revolution and become a leader in AI-powered analytics.
Enroll now in Data and AI (Microsoft Fabric & Azure OpenAI) and future-proof your career.

Who this course is for:

AI Enthusiasts
Data Scientists
Data Engineers
Software Architects
AI/ML Engineers

End-to-End Data & AI: Microsoft Fabric + Azure OpenAI

What you'll learn

Explore related topics

Course content

Introduction1 lecture • 5min

Important Concepts2 lectures • 24min

Microsoft Fabric Lakehouse and Warehouse [Data Engineering]14 lectures • 3hr 10min

Classical ML with Fabric [ML Engineering]10 lectures • 2hr 35min

Predictive AI with Fabric: Using Prebuilt Azure AI Models [AI Engineering]4 lectures • 54min

Gen AI Primer (GAI-101) [GenAI Engineering]7 lectures • 1hr 12min

Generative AI (Azure OpenAI) with Fabric [GenAI Engineering]6 lectures • 1hr 29min

Requirements

Description

Who this course is for: