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.
Prerequisites
Before attending this course, students must have:
- A fundamental knowledge of Microsoft Azure.
- Experience of writing Python code to work with data, using libraries such as Numpy, Pandas, and Matplotlib.
- Understanding of data science; including how to prepare data, and train machine learning models using common machine learning libraries such as Scikit-Learn, PyTorch, or Tensorflow.
Duration
3 days. Hands on.
Course Objectives
Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This 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.
Course Content
Module 1: Introduction to Azure Machine Learning
Getting Started with Azure Machine Learning
Azure Machine Learning Tools
Lab : Creating an Azure Machine Learning Workspace
Lab : Working with Azure Machine Learning Tools
After completing this module, you will be able to:
• Provision an Azure Machine Learning workspace
• Use tools and code to work with Azure Machine Learning
Module 2: No-Code Machine Learning with Designer
Training Models with Designer
Publishing Models with Designer
Lab : Creating a Training Pipeline with the Azure ML Designer
Lab : Deploying a Service with the Azure ML Designer
After completing this module, you will be able to:
• Use designer to train a machine learning model
• Deploy a Designer pipeline as a service
Module 3: Running Experiments and Training Models
Introduction to Experiments
Training and Registering Models
Lab : Running Experiments
Lab : Training and Registering Models
After completing this module, you will be able to:
• Run code-based experiments in an Azure Machine Learning workspace
• Train and register machine learning models
Module 4: Working with Data
Working with Datastores
Working with Datasets
Lab : Working with Datastores
Lab : Working with Datasets
After completing this module, you will be able to:
• Create and consume datastores
• Create and consume datasets
Module 5: Compute Contexts
Working with Environments
Working with Compute Targets
Lab : Working with Environments
Lab : Working with Compute Targets
After completing this module, you will be able to:
• Create and use environments
• Create and use compute targets
Module 6: Orchestrating Operations with Pipelines
Introduction to Pipelines
Publishing and Running Pipelines
Lab : Creating a Pipeline
Lab : Publishing a Pipeline
After completing this module, you will be able to:
• Create pipelines to automate machine learning workflows
• Publish and run pipeline services
Module 7: Deploying and Consuming Models
Real-time Inferencing
Batch Inferencing
Lab : Creating a Real-time Inferencing Service
Lab : Creating a Batch Inferencing Service
After completing this module, you will be able to:
• Publish a model as a real-time inference service
• Publish a model as a batch inference service
Module 8: Training Optimal Models
Hyperparameter Tuning
Automated Machine Learning
Lab : Tuning Hyperparameters
Lab : Using Automated Machine Learning
After completing this module, you will be able to:
• Optimize hyperparameters for model training
• Use automated machine learning to find the optimal model for your data
Module 9: Interpreting Models
Introduction to Model Interpretation
Using Model Explainers
Lab : Reviewing Automated Machine Learning Explanations
Lab : Interpreting Models
After completing this module, you will be able to:
• Generate model explanations with automated machine learning
• Use explainers to interpret machine learning models
Module 10: Monitoring Models
Monitoring Models with Application Insights
Monitoring Data Drift
Lab : Monitoring a Model with Application Insights
Lab : Monitoring Data Drift
After completing this module, you will be able to:
• Use Application Insights to monitor a published model
• Monitor data drift