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

Fundamental knowledge of cloud computing concepts, experience in creating cloud resources in Microsoft Azure, using Python for data exploration and visualization, 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