Learn to operate machine learning solutions at cloud scale using Azure Machine Learning. This course leverages your Python and ML knowledge to manage data ingestion, model training, deployment, and monitoring in Microsoft Azure.

Learning Outcomes

• Provision an Azure Machine Learning workspace
• Use tools and code to work with Azure Machine Learning
• Use automated machine learning to train a machine learning model
• Use Azure Machine Learning designer to train a model
• Run code-based experiments in an Azure Machine Learning workspace
• Train and register machine learning models
• Create and use datastores
• Create and use datasets
• Create and use environments
• Create and use compute targets
• Create pipelines to automate machine learning workflows
• Publish and run pipeline services
• Publish a model as a real-time inference service
• Publish a model as a batch inference service
• Describe techniques to implement continuous integration and delivery
• Optimize hyperparameters for model training
• Use automated machine learning to find the optimal model for your data
• Apply differential privacy to data analysis
• Use explainers to interpret machine learning models
• Evaluate models for fairness
• Use Application Insights to monitor a published model
• Monitor data drift

Prerequisites

Fundamental knowledge of cloud computing concepts and experience in data science and machine learning tools. Specifically: creating cloud resources in Microsoft Azure, using Python to explore and visualize data, training and validating machine learning models with frameworks like Scikit-Learn, PyTorch, and TensorFlow, and working with containers.

Prerequisite Courses

• Explore Microsoft cloud concepts
• Create machine learning models
• Administer containers in Azure
• Microsoft Azure AI Fundamentals