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