DP-604: Implement a data science and machine learning solution for AI with Microsoft Fabric

Course Description:

Unlock the potential of artificial intelligence by building scalable data science and machine learning solutions using Microsoft Fabric. This instructor-led course guides data scientists, engineers, and AI practitioners through the entire AI lifecycle— from data preparation and model training to deployment and monitoring—leveraging Fabric’s unified analytics platform.

Explore how to integrate Azure Machine Learning, Spark-based data processing, and Fabric notebooks to develop intelligent AI solutions that drive actionable insights. Learn best practices for managing data, creating machine learning pipelines, and operationalizing models to accelerate business innovation.

This course includes 40% to 50% hands-on activities, enabling practical experience with data science and AI workflows in Microsoft Fabric.

Target Audience:

This course is designed for:

• Data scientists, machine learning engineers, and AI developers building intelligent solutions with Microsoft Fabric

• Data engineers supporting AI and ML workflows in unified analytics environments

• IT professionals and analysts interested in applying AI models to business challenges

• Individuals preparing for advanced certifications related to Microsoft Fabric AI and machine learning

Prerequisites:
Familiarity with basic data science concepts, Python or R programming, and cloud data services is recommended. Prior experience with Azure or ML frameworks is beneficial.

Course Outline:

Module 1: Introduction to AI, Data Science, and Microsoft Fabric

• Understand the AI and data science lifecycle within Microsoft Fabric

• Explore Fabric’s integrated tools for data engineering, experimentation, and model management

• Review core concepts of machine learning, deep learning, and AI workloads in the cloud

Module 2: Preparing Data for Machine Learning in Microsoft Fabric

• Ingest and clean data using Spark notebooks and Fabric’s data pipelines

• Explore feature engineering techniques and data transformations for ML readiness

• Manage data versioning and lineage with OneLake and Delta tables

Module 3: Building and Training Machine Learning Models

• Use Fabric notebooks with Python and popular ML libraries (e.g., scikit-learn, TensorFlow)

• Develop supervised, unsupervised, and reinforcement learning models

• Implement hyperparameter tuning, cross-validation, and model evaluation techniques

Module 4: Deploying and Operationalizing AI Solutions

• Deploy machine learning models as REST APIs within Microsoft Fabric

• Automate model retraining and pipeline orchestration for continuous learning

• Monitor model performance, drift, and data quality over time

Module 5: Integrating AI Insights with Analytics and Business Intelligence

• Embed AI-generated insights into Power BI dashboards and reports

• Combine real-time analytics with predictive modeling for proactive decision-making

• Collaborate across teams to operationalize AI in business workflows

Module 6: Governance, Security, and Compliance for AI Solutions

• Apply security best practices for AI data and model management

• Implement compliance controls, auditing, and ethical AI considerations

• Ensure responsible AI deployment aligned with organizational policies

Delivery Format:

• Instructor-led training with expert demonstrations

• 40% to 50% hands-on exercises in Microsoft Fabric’s data science and AI environment

• Real-world scenarios to develop applied AI and machine learning skills