Data And Ai (Microsoft Fabric)

Azure OpenAI with Microsoft Fabric, Data Lakehouse, Data Warehouse, OneLake, Apache Spark, MLflow, Synapse ML

What you'll learn

Microsoft Fabric

Data Analytics

AI/ML with Microsoft Fabric

Generative AI (Azure OpenAI) with Microsoft Fabric

Requirements

Programming Experience Required

Description

Are you ready to revolutionize how you manage and analyze data? Welcome to Data and AI (Microsoft Fabric), the ultimate guide to mastering Microsoft’s cutting-edge unified analytics platform. This course is designed to empower data professionals, cloud enthusiasts, and business leaders with the knowledge and skills to harness the full potential of Microsoft Fabric for modern data and AI solutions.Microsoft Fabric is more than just a tool—it’s a transformative platform that integrates all essential data capabilities into a single, cohesive ecosystem. From Data Engineering to Real-Time Analytics, Data Science, Data Factory, Databases, and Data Warehousing, Fabric offers an all-in-one solution for handling complex data workflows and driving impactful insights.At the heart of Microsoft Fabric lies OneLake, a unified storage solution designed to eliminate data silos and streamline collaboration across teams. OneLake ensures seamless data discovery, sharing, and policy enforcement, making it the cornerstone of efficient and secure data management. Whether you’re a data engineer managing ETL pipelines, a data scientist building AI models, or a business analyst generating reports, OneLake simplifies your workflows while enhancing productivity.What You’ll LearnIn this course, we’ll take you through a comprehensive exploration of Microsoft Fabric, equipping you with:An in-depth understanding of the platform: Learn about Fabric’s architecture, its SaaS-based approach, and how it unifies data workflows under one roof.Core tools and functionalities: Discover how to leverage Data Science tools for building and deploying machine learning models.OneLake’s transformative power: Understand how unified storage changes the game for data management, enabling efficient data sharing and robust security measures.Practical, hands-on experience: Follow step-by-step demonstrations and real-world scenarios to apply Microsoft Fabric’s features effectively.Why This Course is For YouWhether you’re just starting your journey in data and AI or are a seasoned professional looking to adopt the latest technologies, this course is tailored to meet your needs. It’s perfect for:Data Engineers: Streamline your workflows and build scalable pipelines with ease.Data Scientists: Unlock powerful tools for developing and deploying AI models.Cloud Enthusiasts: Explore Fabric’s integration with Microsoft’s ecosystem and the cloud-first approach.Business Leaders: Gain insights into how Fabric can drive data-driven decision-making in your organization.Course HighlightsReal-world applications of Microsoft Fabric and its suite of tools.Easy-to-follow demonstrations and hands-on exercises.Practical insights into solving modern enterprise data challenges.Step-by-step guidance on implementing unified data solutions.What You’ll AchieveBy the end of this course, you’ll have a solid grasp of Microsoft Fabric’s capabilities and how to apply them to real-world scenarios. You’ll be ready to integrate this powerful platform into your projects and organizations, unlocking new levels of efficiency, collaboration, and innovation in data and AI.Don’t miss out on the opportunity to stay ahead in the ever-evolving world of technology. Enroll in Data and AI (Microsoft Fabric) today and take the first step toward becoming a leader in the field of unified analytics and data management!

Overview

Section 1: Introduction

Lecture 1 Course Introduction

Section 2: Important Concepts

Lecture 2 What is Microsoft Fabric?

Lecture 3 What is Data Lakehouse and OneLake?

Lecture 4 OneLake Explorer

Lecture 5 Fabric Licensing Guide

Section 3: Microsoft Fabric Lakehouse and Warehouse

Lecture 6 Data Analytical Engines in Fabric

Lecture 7 Getting Started with Fabric Portal

Lecture 8 Lab: Data Transformation with Apache Spark, SQL and T-SQL (Hands-On Lab)

Lecture 9 Lab: Default Semantic Model in Lakehouse (Hands-On Lab)

Lecture 10 Why Data Warehouse matters?

Lecture 11 Lab: Loading Data from a Lakehouse to a Warehouse (Hands-On Lab)

Lecture 12 Introduction to Semantic Models

Lecture 13 Lab: Creating a Facts and Dimensions Semantic Model (Hands-On Lab)

Lecture 14 Introduction to medallion architecture

Lecture 15 Lab: Implementing Medallion Architecture (Hands-On Lab)

Lecture 16 Introduction to Dataflows and Fabric Pipelines

Lecture 17 Lab: Building Dataflow and Pipeline (Hands-On Lab)

Section 4: Predictive AI with Fabric: Building Your Own/Custom Models

Lecture 18 Introduction to building your own custom models with Fabric

Lecture 19 Understanding Basic ML Concepts

Lecture 20 Understanding Linear Algorithms

Lecture 21 Understanding Non-Linear Algorithms

Lecture 22 Lab: Building Model for Diabetes Prediction (Hands-On Lab)

Lecture 23 Lab: Using Diabetes Predictor With Fabric Pipelines (Hands-On Lab)

Lecture 24 Lab: Model and Metrics Logging using MLflow (Hands-On Lab)

Lecture 25 Lab: Hyperparameter Tuning using Hyperopt and MLflow (Hands-On Lab)

Lecture 26 Introduction to Deep Learning Models

Lecture 27 Lab: Building Deep Learning Model with Fabric (Hands-On Lab)

Section 5: Predictive AI with Fabric: Using Prebuilt Azure AI Models

Lecture 28 Introduction to Azure AI and Microsoft Fabric

Lecture 29 Project Overview: Sentiment Analysis for an E-Commerce Platform

Lecture 30 Lab: Getting Comfy with Azure Language Service (Hands-On Lab)

Lecture 31 Lab: Building Sentiment Analysis Workflow in Fabric (Hands-On Lab)

Section 6: Gen AI Primer (GAI-101)

Lecture 32 What is Generative AI?

Lecture 33 What is Azure OpenAI?

Lecture 34 What is Prompt Engineering?

Lecture 35 Building Blocks of a Prompt

Lecture 36 Chain Of Thoughts Prompting Technique

Lecture 37 Controlling Output Through Prompt Engineering

Lecture 38 Lab: Getting Started with Azure OpenAI (Hands-On Lab)

Lecture 39 Lab: ChatCompletions API to call GPT Engine via Code (Hands-On Lab)

Lecture 40 What are Vector Embeddings?

Lecture 41 Lab: Making use of Vector Embeddings via Code (Hands-On Lab)

Lecture 42 What is RAG (Retrieval Augmented Generation)?

Section 7: Generative AI (Azure OpenAI) with Fabric

Lecture 43 Introduction to GenAI and Fabric

Lecture 44 Introduction to RAG with Microsoft Fabric

Lecture 45 Lab: Deploying Resources in Azure (Hands-On Lab)

Lecture 46 Lab: RAG Implementation in Fabric (Hands-On Lab)

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