Microsoft Certified: Azure Data Scientist Associate (DP100)

Designing and Implementing a Data Science Solution on Azure (DP-100T01)

Module 1: Introduction to the Azure Machine Learning SDK

• Azure Machine Learning workspaces
• Azure Machine Learning tools and interfaces
• Azure Machine Learning experiments

Module 2: Use Automated Machine Learning in Azure Machine Learning

• What is machine learning?
• What is Azure Machine Learning studio?
• What is Azure Automated Machine Learning?
• Understand the AutoML process

Module 3: Create a classification model with Azure Machine Learning designer

• Identify classification machine learning scenarios
• What is Azure Machine Learning?
• What is Azure Machine Learning designer?
• Understand steps for classification

Module 4: Train a machine learning model with Azure Machine Learning

• Run a training script
• Using script parameters
• Registering models

Module 5: Work with Data in Azure Machine Learning

• Introduction to datastores
• Use datastores
• Introduction to datasets
• Use datasets

Module 6: Work with Compute in Azure Machine Learning

• Introduction to environments
• Introduction to compute targets
• Create compute targets
• Use compute targets

Module 7: Orchestrate machine learning with pipelines

• Introduction to pipelines
• Pass data between pipeline steps
• Reuse pipeline steps
• Publish pipelines
• Use pipeline parameters
• Schedule pipelines

Module 8: Deploy real-time machine learning services with Azure Machine Learning

• Deploy a model as a real-time service
• Consume a real-time inferencing service
• Troubleshoot service deployment

Module 9: Deploy batch inference pipelines with Azure Machine Learning

• Creating a batch inference pipeline
• Publishing a batch inference pipeline

Module 10: Tune hyperparameters with Azure Machine Learning

• Defining a search space
• Configuring sampling
• Configuring early termination
• Running a hyperparameter tuning experiment

Module 11: Automate machine learning model selection with Azure Machine Learning

• Automated machine learning tasks and algorithms
• Preprocessing and featurization
• Running automated machine learning experiments

Module 12: Explore differential privacy

• Understand differential privacy
• Configure data privacy parameters

Module 13: Explain machine learning models with Azure Machine Learning

• Feature importance
• Using explainers
• Creating explanations
• Visualizing explanations

Module 14: Detect and mitigate unfairness in models with Azure Machine Learning

• Consider model fairness
• Analyze model fairness with Fairlearn
• Mitigate unfairness with Fairlearn

Module 15: Monitor data drift with Azure Machine Learning

• Creating a data drift monitor
• Scheduling alerts

Module 16: Monitor models with Azure Machine Learning

• Enable Application Insights
• Capture and view telemetry