• 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
• Explore developer tools for workspace interaction
• Make data available in Azure Machine Learning
• Work with compute targets in Azure Machine Learning
• Work with environments in Azure Machine Learning
• Find the best classification model with Automated Machine Learning
• Track model training in Jupyter notebooks with MLflow
• Run a training script as a command job in Azure Machine Learning
• Track model training with MLflow in jobs
• Run pipelines in Azure Machine Learning
• Perform hyperparameter tuning with Azure Machine Learning
• Deploy a model to a managed online endpoint
• Deploy a model to a batch endpoint

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.

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