Designing and Implementing Data Science Solutions on Microsoft Azure

1: Design a machine learning solution

• Design a data ingestion strategy for machine learning projects
• Design a machine learning model training solution
• Design a model deployment solution

2: Explore the Azure Machine Learning workspace

• Explore Azure Machine Learning workspace resources and assets
• Explore developer tools for workspace interaction

3: Make data available in Azure Machine Learning

• Make data available in Azure Machine Learning

4: Work with compute in Azure Machine Learning

• Work with compute targets in Azure Machine Learning
• Work with environments in Azure Machine Learning

5: Use no-code machine learning with the Azure Machine Learning Designer

• Explore data with the Azure Machine Learning Designer
• Train and compare models with the Azure Machine Learning Designer

6: Automate machine learning model selection with Azure Machine Learning

• Explore Automate Machine Learning
• Find the best classification model with Automated Machine Learning

7: Use notebooks for experimentation in Azure Machine Learning

• Track model training in Jupyter notebooks with MLflow

8: Train models with scripts in Azur Machine Learning

• Run a training script as a command job in Azure Machine Learning
• Track model training with MLflow in jobs

9: Optimize model training in Azure Machine Learning

• Run pipelines in Azure Machine Learning
• Perform hyperparameter tuning with Azure Machine Learning

10: Manage and review models in Azure Machine Learning

• Register an MLflow model in Azure Machine Learning
• Manage and compare models in Azure Machine Learning

11: Deploy and consume models with Azure Machine Learning

• Deploy a model to a managed online endpoint
• Deploy a model to a batch endpoint

12: Design a machine learning operations (MLOps) solution

• Design a machine learning operations (MLOps) solution