Learn to operate machine learning solutions at cloud scale using Azure Machine Learning. This course covers data ingestion, model training, deployment, and monitoring with Azure ML and MLflow.

Learning Outcomes

• Design a data ingestion strategy for machine learning projects
• Design a machine learning model training solution
• Design a model deployment solution
• Explore Azure Machine Learning workspace resources and assets
• Use developer tools for workspace interaction
• Make data available in Azure Machine Learning
• Work with compute targets and environments in Azure Machine Learning
• Find the best classification model with Automated Machine Learning
• Track model training with MLflow in Jupyter notebooks and jobs
• Run training scripts and pipelines in Azure Machine Learning
• Perform hyperparameter tuning
• Deploy models to managed online and batch endpoints

Prerequisites

Successful Azure Data Scientists start this role with a fundamental knowledge of cloud computing concepts, and experience in general data science and machine learning tools and techniques. Specifically:

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

To gain these prerequisite skills, take the following free online training before attending the course:

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

If you are completely new to data science and machine learning, please complete Microsoft Azure AI Fundamentals first.