👨‍🏫 Instructor-Led Training

DP-100: Designing and Implementing a Data Science Solution on Azure

Course Code: DP-100
Duration: 4 Days
Level: Intermediate
Category: Database Administration

Course Overview

Course Description

Elevate your data science career by mastering Azure Machine Learning for end-to-end data science solutions. This four-day, instructor-led course empowers data scientists to design, build, deploy, and monitor machine learning models at cloud scale. You’ll gain in-depth experience crafting ML pipelines, leveraging MLflow tracking, deploying models to batch and real-time endpoints, and integrating Responsible AI practices. This course aligns with the Microsoft Certified: Azure Data Scientist Associate certification, reinforcing skills with real-world architecture and production-ready workflows.


Target Audience

Ideal for:

  • Data Scientists, ML Engineers, and AI Developers deploying and operating ML solutions using Azure Machine Learning and MLflow

  • Professionals preparing for the DP100 Azure Data Scientist Associate certification exam

Prerequisites:

  • Proficiency in Python and familiarity with machine learning frameworks such as ScikitLearn, PyTorch, or TensorFlow

  • Basic understanding of cloud concepts and Azure services like storage and compute 


Course Outline 

Module 1: Design and Create an Azure Machine Learning Workspace

  • Set up and configure an Azure Machine Learning workspace using Studio, Python SDK v2, and Azure CLI

  • Organize data, compute targets, environments, and registered models for reproducible workflows 

Module 2: Prepare Data and Build Experiments

  • Register data assets and use datasets and datastores to feed ML workflows

  • Configure experiment compute targets (compute instances, clusters) and encapsulate dependencies in environments

Module 3: Train, Track, and Optimize Models

  • Implement custom code experiments and track metrics with MLflow

  • Use Automated ML for rapid model generation and run hyperparameter tuning

  • Create training pipelines for modular and scalable execution 

Module 4: Manage and Evaluate ML Models

  • Register models from both code and Automated ML results

  • Analyze model metrics and use Responsible AI dashboards to assess fairness, interpretability, and bias

Module 5: Deploy to Real-Time and Batch Endpoints

  • Deploy registered models as managed online endpoints with scaling, authentication, and versioning

  • Configure batch endpoints to process large datasets in a serverless fashion 

Module 6: Build ML Pipelines and Operationalize MLOps

  • Design production workflows with pipeline steps that integrate compute, data, and model assets

  • Incorporate retraining, CI/CD patterns, and orchestration best practices

Module 7: Monitor Models and Enable Responsible AI

  • Monitor deployed models for performance with Azure Monitor, MLflow, and built-in drift detection

  • Implement model governance, logging, and automated retraining for production reliability


HandsOn Experience

Expect 40–50% of class time dedicated to practical exercises. You’ll build complete Azure data science solutions—from workspace setup to pipeline orchestration, model deployment, and monitoring—with instructor-guided scenarios.


Skills You’ll Gain

By the end of the course, you’ll be able to:

  • Structure and manage Azure Machine Learning workspaces and assets

  • Train, track, and optimize models using MLflow, Automated ML, and custom pipelines

  • Deploy models as real-time and batch endpoints with version control and security

  • Embed Responsible AI capabilities and monitor model drift and performance

  • Operationalize ML solutions with pipelines, retraining strategies, and CI/CD integration

Hands-On Labs

This course includes practical, hands-on laboratory exercises to reinforce your learning:

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