Microsoft Fabric Lessons

08/03/2026

Want to learn Microsoft Fabric, here is a series of lessons that will help you to get started with Microsoft Fabric and its components.

Fabric Items Naming Conventions – Coming soon

  • Lesson 1 – What is Microsoft Fabric and Fabric Terminologies?
  • Lesson 2 – Microsoft Fabric Licensing Model
  • Lesson 3 – Getting started with Microsoft Fabric
  • Lesson 4 – Fabric Workspaces and how to create one?
  • Lesson 5 – Workspace roles and access control
  • Lesson 6 – What is Microsoft Fabric Lakehouse?
  • Lesson 7 – Getting started with Microsoft Fabric Lakehouse
  • Lesson 8 – Lakehouse architecture
  • Lesson 9 – Shortcuts in a Lakehouse
  • Lesson 10 – Ingest, transform, analyze and visualize data in a Lakehouse using Microsoft Fabric
  • Lesson 11 – Ingest, transform, analyze and visualize data in a Lakehouse using Notebooks
  • Lesson 12 – What is OneLake?
  • Lesson 13 – Microsoft Fabric OneLake vs Lakehouse
  • Lesson 14 – What is OneLake file explorer?
  • Lesson 15 – Access data in OneLake using API
  • Lesson 16 – Understanding OneLake Security
  • Lesson 17 – What is OneLake Data Hub?
  • Lesson 18 – Introduction to Data Pipelines in Microsoft Fabric
  • Lesson 19 – Quick overview on Data Pipeline connectors
  • Lesson 20 – Ingest data using Copy Activity in Microsoft Fabric
  • Lesson 21 – What are Dataflows Gen2 in Microsoft Fabric?
  • Lesson 22 – Quick overview on Dataflow Gen2 connectors
  • Lesson 23 – Ingest data using Dataflows Gen2 in Microsoft Fabric
  • Lesson 24 – Migration Path for Azure Data Factory to Microsoft Fabric
  • Lesson 25 – Migration Path for Power BI Dataflow Gen1 to Dataflow Gen2 in Microsoft Fabric
  • Lesson 26 – Upskill your Mapping Data flow transformation knowledge to Dataflow Gen2
  • Lesson 27 – Using Notebooks with Microsoft Fabric
  • Lesson 28 – Lifecycle management with Microsoft Fabric
  • Lesson 29 – Microsoft Fabric and Git Integration
  • Lesson 30 – Using Deployment Pipelines with Microsoft Fabric
  • Lesson 31 – Lakehouse and Delta Tables
  • Lesson 32 – Using SQL analytics endpoint with Lakehouse
  • Lesson 33 – Using Power BI with Lakehouse
  • Lesson 34 – Introduction to Data Science with Microsoft Fabric
  • Lesson 35 – Creating a Machine Learning Model with Microsoft Fabric
  • Lesson 36 – Data Wrangler in Microsoft Fabric
  • Lesson 37 – Pre-Built AI Models in Microsoft Fabric
  • Lesson 38 – Train Models with Spark Mlib with Microsoft Fabric
  • Lesson 39 – Train Models with SynapseML
  • Lesson 40 – Train models with Scikit-Learn
  • Lesson 41 – Getting started with Apache Spark in Microsoft Fabric
  • Lesson 42 – What is semantic link in Microsoft Fabric
  • Lesson 43 – How to choose a data store in Microsoft Fabric?
  • Lesson 44 – Load data into Warehouse in Microsoft Fabric (file upload, copy tool, copy activity, dataflow, Notebooks)
  • Lesson 45 – Create Power BI Semantic Model from Warehouse in Microsoft Fabric
  • Lesson 46 – How to apply row-level, column-level and dynamic data masking in Microsoft Fabric
  • Lesson 47 – Manage and monitor performance of the Warehouse in Microsoft Fabric
  • Lesson 48 – How to share a Warehouse using Microsoft Fabric?
  • Lesson 49 – What is clone tables and how to do that using Fabric Portal?
  • Lesson 50 – Cost savings with Fabric Data Warehousing
  • Lesson 51 – Microsoft Fabric vs. Azure Synapse Analytics
  • Lesson 52 – Microsoft Fabric vs. Azure Databricks
  • Lesson 53 – Microsoft Fabric vs. Snowflake
  • Lesson 54 – Migrate Synapse Dedicated SQL pool Warehouse to Microsoft Fabric
  • Lesson 55 – Quick Introduction to Real-Time Intelligence
  • Lesson 56 – Real-Time Intelligence vs. Azure Data Explorer
  • Lesson 57 – Create and manage Event Streams with Microsoft Fabric
  • Lesson 58 – Getting started with Kusto Query Language (KQL)
  • Lesson 59 – Functions in KQL
  • Lesson 60 – Visualize KQL Database data in Power BI
  • Lesson 61 – Quick introduction to Data Activator
  • Lesson 62 – Using Triggers with Data Activator
  • Lesson 63 – Using Copilot in Microsoft Fabric
  • Lesson 64 – Using SQL Projects for Warehouse in Microsoft Fabric
  • Lesson 65 – What is Fabric IQ? The Intelligent Layer of Microsoft Fabric
  • Lesson 66 – Understanding the Fabric IQ Architecture
  • Lesson 67 – Fabric IQ vs Traditional Semantic Models
  • Lesson 68 – How Fabric IQ Enables AI Agents
  • Lesson 69 – Creating your First Ontology in Microsoft Fabric (both using OneLake and Semantic Model)
  • Lesson 70 – Understanding Ontology Concepts in Microsoft Fabric
  • Lesson 71 – Creating Entity Types in Fabric Ontology
  • Lesson 72 – Defining relationships in Fabric Ontology
  • Lesson 73 – Binding Data Sources to Ontology
  • Lesson 74 – Visualizing Data Relationships with Fabric Graph
  • Lesson 75 – Introduction to Data Agents in Microsoft Fabric
  • Lesson 76 – Creating a Data Agent in Microsoft Fabric
  • Lesson 77 – Data Agents vs Copilot in Microsoft Fabric
  • Lesson 78 – Operational Agents in Fabric IQ
  • Lesson 79 – Monitoring Data Agents and Agent Governance
  • Lesson 80 – How Copilot understands your Data using Semantic Models
  • Lesson 81 – Natural Language Analytics with Copilot in Microsoft Fabric
  • Lesson 82 – Databricks Genie vs. Copilot vs. Data Agents
  • Lesson 83 – Direct Lake Architecture Explained
  • Lesson 84 – Direct Lake vs. Import vs. Direct Query
  • Lesson 85 – Direct Lake Internals – Query Execution
  • Lesson 86 – Performance Optimization for Direct Lake
  • Lesson 87 – Direct Lake Security
  • Lesson 88 – Semantic Models in the Fabric Era
  • Lesson 89 – Building Enterprise Semantic Models in Fabric
  • Lesson 90 – DAX Query Performance in Fabric
  • Lesson 91 – What is Mirroring in Microsoft Fabric
  • Lesson 92 – Supported Sources for Fabric Mirroring
  • Lesson 93 – Mirroring vs ETL Pipelines
  • Lesson 94 – Mirroring with Azure SQL Database
  • Lesson 95 – Mirroring with Azure SQL Managed Instance
  • Lesson 96 – Mirroring with Azure Cosmos DB
  • Lesson 97 – Mirroring with Azure Databricks
  • Lesson 98 – Mirroring with Google BigQuery
  • Lesson 99 – Mirroring with Oracle
  • Lesson 100 – Mirroring with PostgreSQL
  • Lesson 101 – Mirroring with SAP
  • Lesson 102 – Mirroring with Snowflake
  • Lesson 103 – Mirroring with SQL Server
  • Lesson 104 – Implement Open Mirroring with MS Fabric
  • Lesson 105 – Monitoring Mirrored Databases
  • Lesson 106 – Introduction to Fabric Extensibility Toolkit
  • Lesson 107 – Building Custom Fabric Workloads
  • Lesson 108 – Consuming Custom Fabric Workloads
  • Lesson 109 – Fabric APIs and Developer Integrations
  • Lesson 110 – Automating Fabric Workflows with APIs
  • Lesson 111 – Microsoft Fabric Tenant Administration
  • Lesson 112 – Understanding Fabric Capacity Management
  • Lesson 113 – Monitoring Fabric Capacity Usage
  • Lesson 114 – Managing Fabric Workspaces at Scale
  • Lesson 115 – Fabric Security Model for Administrators
  • Lesson 116 – Auditing and Activity Logs in MS Fabric
  • Lesson 117 – Governance Best Practices for MS Fabric
  • Lesson 118 – Microsoft Purview Integration with MS Fabric
  • Lesson 119 – Data Lineage in Microsoft Fabric
  • Lesson 120 – Data Catalog and Discovery in Fabric
  • Lesson 121 – Monitoring Fabric Workloads
  • Lesson 122 – Query Performance Monitoring with MS Fabric
  • Lesson 123 – Fabric Cost Optimization Strategies
  • Lesson 124 – Troubleshooting Fabric Performance Issues
  • Lesson 125 – Capacity Planning for Microsoft Fabric
  • Lesson 126 – Integrating Microsoft Fabric with Azure Databricks
  • Lesson 127 – Integrating MS Fabric with Azure AI Services
  • Lesson 128 – Using MS Fabric with Azure Event Hubs
  • Lesson 129 – Using MS Fabric with Azure Stream Analytics
  • Lesson 130 – Integrating Fabric with Azure Machine Learning
  • Lesson 131 – Implementing Medallion Architecture in Fabric
  • Lesson 132 – Building Data Mesh with Microsoft Fabric
  • Lesson 133 – Data Sharing across Domains with MS Fabric
  • Lesson 134 – Designing Multi-Tenant Fabric Platforms
  • Lesson 135 – CI/CD for Microsoft Fabric
  • Lesson 136 – Automating Fabric Deployments
  • Lesson 137 – Fabric Environment Promotion Strategies
  • Lesson 138 – Version Control Best Practices in Fabric
  • Lesson 139 – Testing strategies for Fabric Solutions
    Lesson 140 – Introduction to Microsoft Fabric SQL Database: Understanding the New Operational Database Experience
  • Lesson 141 – Microsoft Fabric SQL Database vs Data Warehouse vs Lakehouse: Choosing the Right Data Store for Your Workload
  • Lesson 142 – Building Modern Operational Applications with Microsoft Fabric SQL Database: Architecture, Design and Best Practices
  • Lesson 143 – Mirroring Operational SQL Databases into OneLake: Building a Unified Analytics Platform with Microsoft Fabric
  • Lesson 144 – Understanding Built-in Mirroring in Microsoft Fabric SQL Database: How Near Real-Time Data Replication Works
  • Lesson 145 – Implementing Full-Text Search in Microsoft Fabric SQL Database for Fast and Flexible Data Retrieval
  • Lesson 146 – Configuring and Managing Database Settings using ALTER DATABASE in Microsoft Fabric SQL Database
  • Lesson 147 – Managing Compute Resources, vCore Limits and Performance for Microsoft Fabric SQL Database
  • Lesson 148 – Protecting Your Data with Recovery, Restore and Recycle Bin Capabilities in Microsoft Fabric SQL Database
  • Lesson 149 – Securing Microsoft Fabric SQL Database using Customer-Managed Keys (CMK) and Enterprise Encryption
  • Lesson 150 – Auditing User Activity and Database Operations in Microsoft Fabric SQL Database
  • Lesson 151 – Introduction to Variable Libraries in Microsoft Fabric: Centralizing Configuration Across Data Solutions
  • Lesson 152 – Managing Development, Test and Production Environments using Variable Libraries in Microsoft Fabric
  • Lesson 153 – Simplifying Connectivity using Connection References in Microsoft Fabric Pipelines and Data Factory
  • Lesson 154 – Reusing Fabric Resources Efficiently with Item References Across Workspaces and Projects
  • Lesson 155 – Implementing CI/CD Pipelines using Variable Libraries for Enterprise Microsoft Fabric Deployments
  • Lesson 156 – Promoting Microsoft Fabric Solutions Across Environments Without Modifying Pipelines
  • Lesson 157 – Getting Started with Apache Airflow in Microsoft Fabric: Modern Workflow Orchestration Explained
  • Lesson 158 – Creating, Scheduling and Managing Apache Airflow DAGs in Microsoft Fabric
  • Lesson 159 – Using Variable Libraries with Apache Airflow for Secure and Environment-Aware Workflow Management
  • Lesson 160 – Apache Airflow vs Microsoft Fabric Data Pipelines: Choosing the Right Orchestration Platform
  • Lesson 161 – Introduction to AI Functions in Microsoft Fabric: Bringing Generative AI into Data Engineering and Analytics
  • Lesson 162 – Using AI Functions in SQL to Perform Summarization, Classification and Data Enrichment in Microsoft Fabric
  • Lesson 163 – Leveraging AI Functions in PySpark for Intelligent Data Processing and Machine Learning Workloads
  • Lesson 164 – Applying AI Functions in Pandas DataFrames for AI-Powered Data Transformation and Analysis
  • Lesson 165 – Integrating AI Functions into Dataflows Gen2 for No-Code Intelligent Data Preparation
  • Lesson 166 – Monitoring AI Token Consumption, Usage Limits and Cost Optimization in Microsoft Fabric
  • Lesson 167 – Choosing the Right AI Model in Microsoft Fabric: GPT-5 Mini, GPT-5.1 and Other Supported Models
  • Lesson 168 – OneLake Security Deep Dive: Understanding the Enterprise Security Model in Microsoft Fabric
  • Lesson 169 – Understanding OneLake Roles, Permissions and Access Management in Microsoft Fabric
  • Lesson 170 – Implementing Folder-Level Security in OneLake to Protect Sensitive Data
  • Lesson 171 – Implementing Table-Level Security in OneLake for Secure Data Access and Governance
  • Lesson 172 – Securing Mirrored Databases in OneLake: Permissions, Governance and Access Control Best Practices
  • Lesson 173 – Exploring the Extended Capabilities of Microsoft Fabric Mirroring for Enterprise Data Integration
  • Lesson 174 – Using Mirroring with Database Views to Simplify Data Integration and Improve Performance
  • Lesson 175 – Understanding Delta Change Data Feed (CDF) and Incremental Data Processing in Microsoft Fabric
  • Lesson 176 – Configuring Selective Table Mirroring to Optimize Storage, Performance and Cost
  • Lesson 177 – Optimizing Performance for Mirrored Databases in Microsoft Fabric
  • Lesson 178 – Securing Mirrored Data Sources with Enterprise Authentication, Authorization and Governance
  • Lesson 179 – Understanding the Cost Model for Microsoft Fabric Mirroring and Strategies for Cost Optimization
  • Lesson 180 – Understanding the Open Mirroring Architecture and Extending Microsoft Fabric to External Data Platforms
  • Lesson 181 – Introduction to Database Hub in Microsoft Fabric: Managing Databases from a Unified Experience
  • Lesson 182 – Managing Multiple Operational and Analytical Databases using Database Hub in Microsoft Fabric
  • Lesson 183 – Searching and Discovering Data using Natural Language Across Multiple Databases in Microsoft Fabric
  • Lesson 184 – Understanding Model Context Protocol (MCP) in Microsoft Fabric and Its Role in AI Agent Integration
  • Lesson 185 – Implementing Knowledge Grounding in Microsoft Fabric to Improve AI Accuracy and Reduce Hallucinations
  • Lesson 186 – Best Practices for Designing, Configuring and Governing Fabric IQ Solutions
  • Lesson 187 – Designing Enterprise Ontologies in Microsoft Fabric for AI-Ready Business Knowledge Models
  • Lesson 188 – Building Multi-Agent AI Architectures using Fabric IQ and Intelligent Data Agents
  • Lesson 189 – Optimizing Spark Workloads using Resource Profiles in Microsoft Fabric
  • Lesson 190 – Improving Collaboration with Shared Spark Sessions and Session Sharing in Microsoft Fabric
  • Lesson 191 – Managing Notebook Resources, Dependencies and Libraries for Reproducible Data Science Projects
  • Lesson 192 – Using Git-Aware Lakehouses for Collaborative Development and Version-Controlled Data Engineering
  • Lesson 193 – Organizing Enterprise Workspaces Efficiently using Workspace Tags in Microsoft Fabric
  • Lesson 194 – Configuring Capacity Autoscaling in Microsoft Fabric to Handle Dynamic Workloads Efficiently
  • Lesson 195 – Using the Capacity Metrics App to Monitor Resource Utilization and Optimize Performance
  • Lesson 196 – Monitoring Microsoft Fabric Workspaces using Enterprise Monitoring Dashboards
  • Lesson 197 – Monitoring Capacity Consumption, Compute Usage and Costs in Microsoft Fabric
  • Lesson 198 – Monitoring Performance Across Data Engineering, Data Warehousing and Real-Time Workloads in Microsoft Fabric
  • Lesson 199 – Implementing Branch-Based Development for Collaborative Microsoft Fabric Projects
  • Lesson 200 – Deploying Selected Branches Safely Across Development, Test and Production Environments
  • Lesson 201 – Configuring Deployment Rules for Automated and Consistent Microsoft Fabric Releases
  • Lesson 202 – Managing Semantic Models in Source Control using Tabular Model Definition Language (TMDL)
  • Lesson 203 – Building End-to-End Database Development Workflows using SQL Projects in Microsoft Fabric
  • Lesson 204 – Understanding Business Events in Microsoft Fabric for Event-Driven Data Processing
  • Lesson 205 – Monitoring Eventhouses to Ensure Reliable Real-Time Analytics and Streaming Performance
  • Lesson 206 – Creating and Using KQL Functions in Eventhouse for Reusable Real-Time Analytics
  • Lesson 207 – Building AI-Powered Real-Time Dashboards using Copilot in Microsoft Fabric
  • Lesson 208 – Detecting Streaming Anomalies in Real Time using Microsoft Fabric and Machine Learning
  • Lesson 209 – Automating Microsoft Fabric Administration and Development using the Fabric REST API
  • Lesson 210 – Managing Microsoft Fabric Resources from the Command Line using the Fabric CLI
  • Lesson 211 – Automating Administration, Governance and Deployment using Microsoft Fabric PowerShell
  • Lesson 212 – Developing Microsoft Fabric Solutions Efficiently using the Visual Studio Code Extension
  • Lesson 213 – Designing an Enterprise Landing Zone for Microsoft Fabric: Architecture, Governance and Best Practices
  • Lesson 214 – Designing Production-Ready Microsoft Fabric Workspaces for Scalability, Security and Collaboration
  • Lesson 215 – Designing Multi-Region Microsoft Fabric Architectures for High Availability and Global Deployments
  • Lesson 216 – Planning and Implementing Disaster Recovery Strategies for Microsoft Fabric Solutions
  • Lesson 217 – Understanding Backup, Restore and Data Recovery Options in Microsoft Fabric
  • Lesson 218 – Implementing Enterprise Security Best Practices for Microsoft Fabric Platforms
  • Lesson 219 – Performance Tuning Checklist for Microsoft Fabric: Best Practices for Data Engineering, Warehousing and Analytics
  • Lesson 220 – End-to-End Enterprise Reference Architecture for Microsoft Fabric: Building Secure, Scalable and Future-Ready Data Platforms
  • Lesson 221 – Understanding the End-to-End Data Science Lifecycle in Microsoft Fabric: From Business Problem to Production AI
  • Lesson 222 – Setting Up a Microsoft Fabric Data Science Environment: Workspaces, Lakehouses, Notebooks, Compute and Best Practices
  • Lesson 223 – Data Science Personas in Microsoft Fabric: Roles, Responsibilities and Collaboration Across Engineering, Analytics and AI Teams
  • Lesson 224 – Data Exploration and Exploratory Data Analysis (EDA) using Microsoft Fabric Notebooks
  • Lesson 225 – Data Cleaning Techniques in Microsoft Fabric: Handling Missing Values, Outliers and Inconsistent Data
  • Lesson 226 – Feature Engineering Best Practices in Microsoft Fabric for Machine Learning Projects
  • Lesson 227 – Feature Selection Techniques for Building Accurate Machine Learning Models in Microsoft Fabric
  • Lesson 228 – Encoding, Scaling and Transforming Data for Machine Learning in Microsoft Fabric
  • Lesson 229 – Introduction to Experiments in Microsoft Fabric: Tracking Machine Learning Development Effectively
  • Lesson 230 – Using MLflow in Microsoft Fabric for Experiment Tracking, Metrics and Model Versioning
  • Lesson 231 – Comparing Multiple Machine Learning Experiments and Selecting the Best Performing Model
  • Lesson 232 – Getting Started with AutoML in Microsoft Fabric: Building Machine Learning Models Without Writing Code
  • Lesson 233 – Understanding AutoML Algorithms, Evaluation Metrics and Model Selection in Microsoft Fabric
  • Lesson 234 – Customizing AutoML Experiments with Feature Selection, Validation and Optimization
  • Lesson 235 – Regression Algorithms in Microsoft Fabric: Linear Regression, Decision Trees and Ensemble Models
  • Lesson 236 – Classification Algorithms in Microsoft Fabric: Logistic Regression, Random Forest and Gradient Boosting
  • Lesson 237 – Clustering Techniques in Microsoft Fabric: K-Means, Hierarchical Clustering and Customer Segmentation
  • Lesson 238 – Time Series Forecasting using Microsoft Fabric: Building Forecasting Models for Business Scenarios
  • Lesson 239 – Anomaly Detection Techniques in Microsoft Fabric for Fraud Detection and Predictive Maintenance
  • Lesson 240 – Recommendation Systems in Microsoft Fabric using Collaborative and Content-Based Filtering
  • Lesson 241 – Understanding Machine Learning Evaluation Metrics for Regression Models in Microsoft Fabric
  • Lesson 242 – Evaluating Classification Models using Accuracy, Precision, Recall, F1 Score and ROC Curves
  • Lesson 243 – Cross Validation Techniques for Building Reliable Machine Learning Models in Microsoft Fabric
  • Lesson 244 – Hyperparameter Tuning Strategies for Optimizing Machine Learning Models in Microsoft Fabric
  • Lesson 245 – Registering Machine Learning Models in Microsoft Fabric and Managing Model Versions
  • Lesson 246 – Creating and Managing a Model Registry using MLflow in Microsoft Fabric
  • Lesson 247 – Comparing, Promoting and Retiring Machine Learning Models Throughout Their Lifecycle
  • Lesson 248 – Operationalizing Machine Learning Models in Microsoft Fabric for Enterprise Production Workloads
  • Lesson 249 – Batch Scoring using Microsoft Fabric Notebooks and Scheduled Pipelines
  • Lesson 250 – Building End-to-End Machine Learning Pipelines in Microsoft Fabric
  • Lesson 251 – Automating Retraining of Machine Learning Models using Microsoft Fabric Pipelines
  • Lesson 252 – Monitoring Machine Learning Models for Accuracy Drift, Data Drift and Concept Drift
  • Lesson 253 – Implementing Continuous Integration and Continuous Deployment (CI/CD) for Machine Learning Solutions in Microsoft Fabric
  • Lesson 254 – Responsible AI Principles in Microsoft Fabric: Fairness, Transparency, Privacy and Governance
  • Lesson 255 – Detecting Bias in Machine Learning Models and Improving Model Fairness in Microsoft Fabric
  • Lesson 256 – Model Explainability using SHAP, Feature Importance and Explainable AI Techniques in Microsoft Fabric
  • Lesson 257 – Integrating Azure AI Foundry and Azure OpenAI with Microsoft Fabric Data Science Solutions
  • Lesson 258 – Building Retrieval-Augmented Generation (RAG) Solutions using Microsoft Fabric and OneLake
  • Lesson 259 – Working with Vector Embeddings and Vector Search in Microsoft Fabric
  • Lesson 260 – Using Large Language Models (LLMs) for Data Science Workflows in Microsoft Fabric
  • Lesson 261 – Time Series Feature Engineering and Forecasting Best Practices in Microsoft Fabric
  • Lesson 262 – Demand Forecasting using Microsoft Fabric for Retail and Supply Chain Scenarios
  • Lesson 263 – Predictive Maintenance using Microsoft Fabric and Machine Learning
  • Lesson 264 – Customer Churn Prediction using Microsoft Fabric End-to-End
  • Lesson 265 – Fraud Detection using Microsoft Fabric Machine Learning
  • Lesson 266 – Integrating Machine Learning Predictions into Power BI Reports using Microsoft Fabric
  • Lesson 267 – Publishing Machine Learning Outputs to Lakehouse, Warehouse and OneLake for Enterprise Consumption
  • Lesson 268 – Scheduling Machine Learning Pipelines and Monitoring Operational Workloads in Microsoft Fabric
  • Lesson 269 – Designing an Enterprise Machine Learning Platform using Microsoft Fabric Data Science
  • Lesson 270 – End-to-End MLOps Architecture in Microsoft Fabric: From Data Ingestion to Model Monitoring
  • Lesson 271 – Best Practices for Organizing Data Science Projects, Repositories and Notebooks in Microsoft Fabric
  • Lesson 272 – Cost Optimization Strategies for Data Science Workloads in Microsoft Fabric
  • Lesson 273 – Performance Optimization for Large-Scale Machine Learning Workloads using Spark in Microsoft Fabric
  • Lesson 274 – Security and Governance Best Practices for Enterprise Data Science Solutions in Microsoft Fabric
  • Lesson 275 – Building an End-to-End Sales Forecasting Solution using Microsoft Fabric Data Science
  • Lesson 276 – Developing a Customer Churn Prediction Solution using Microsoft Fabric from Data Preparation to Power BI
  • Lesson 277 – Creating an Intelligent Predictive Maintenance Solution using IoT Data and Microsoft Fabric
  • Lesson 278 – Building a Complete Fraud Detection Platform using Microsoft Fabric Data Science
  • Lesson 279 – Building an Enterprise Recommendation Engine using Microsoft Fabric Machine Learning
  • Lesson 280 – Capstone Project: Designing, Building, Operationalizing and Monitoring an Enterprise AI Solution using Microsoft Fabric Data Science