Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.
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.
Module 1: Introduction to Azure Machine Learning
- Getting Started with Azure Machine Learning
- Azure Machine Learning Tools
Module 2: No-Code Machine Learning with Designer
- Training Models with Designer
- Publishing Models with Designer
Module 3: Running Experiments and Training Models
- Introduction to Experiments
- Training and Registering Models
Module 4: Working with Data
- Working with Datastores
- Working with Datasets
Module 5: Compute Contexts
- Working with Environments
- Working with Compute Targets
Module 6: Orchestrating Operations with Pipelines
- Introduction to Pipelines
- Publishing and Running Pipelines
Module 7: Deploying and Consuming Models
- Real-time Inferencing
- Batch Inferencing
Module 8: Training Optimal Models
- Hyperparameter Tuning
- Automated Machine Learning
Module 9: Interpreting Models
- Introduction to Model Interpretation
- using Model Explainers
Module 10: Monitoring Models
- Monitoring Models with Application Insights
- Monitoring Data Drift
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.
- 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.