DP-100T01: Data Science on Azure: 3 Days Workshop


You will learn how to operate machine learning solutions at cloud scale using Azure Machine Learning.

DP-100T01: Designing and Implementing a Data Science Solution on Azure (Data Scientist) course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.

Target Audience

• This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.



• Creating cloud resources in Microsoft Azure.
• Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow.
• Using Python to explore and visualize data.
• Working with containers


Day 1

• Getting Started with Azure Machine Learning

	1. Introduction to Azure Machine Learning
	2. Working with Azure Machine Learning
	3. Lab: Create an Azure Machine Learning Workspace
• Visual Tools for Machine Learning

	1. Automated Machine Learning
	2. Azure Machine Learning Designer
	3. Lab: Use Automated Machine Learning
	4. Lab: Use Azure Machine Learning Designer
• Running Experiments and Training Models

	1. Introduction to Experiments
	2. Training and Registering Models
	3. Lab: Run Experiments
	4. Lab: Train Models

• Working with Data

	1. Working with Datastores
	2. Working with Datasets
	3. Lab: Work with Data

Day 2

• Working with Compute

	1. Working with Environments
	2. Working with Compute Targets
	3. Lab: Work with Compute
• Orchestrating Operations with Pipelines

	1. Introduction to Pipelines
	2. Publishing and Running Pipelines
	3. Lab: Create a Pipeline
• Deploying and Consuming Models

	1. Real-time Inferencing
	2. Batch Inferencing
	3. Continuous Integration and Delivery
	4. Lab: Create a Real-time Inferencing Service
	5. Lab: Create a Batch Inferencing Service

Day 3

• Training Optimal Models

	1. Hyperparameter Tuning
	2. Automated Machine Learning
	3. Lab: Tune Hyperparameters
	4. Lab: Use Automated Machine Learning from the SDK
• Responsible Machine Learning

	1. Differential Privacy
	2. Model Interpretability
	3. Fairness
	4. Lab: Explore Differential privacy
	5. Lab: Interpret Models
	6. Lab: Detect and Mitigate Unfairness
• Monitoring Models

	1. Monitoring Models with Application Insights
	2. Monitoring Data Drift
	3. Lab: Monitor a Model with Application Insights
	4. Lab : Monitor Data Drift