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DP 100

The Azure Data Scientist applies their knowledge of data science and machine learning to implement and run machine learning workloads on Azure; in particular, using Azure Machine Learning Service. This entails planning and creating a suitable working environment for data science workloads on Azure, running data experiments and training predictive models, managing and optimizing models, and deploying machine learning models into production.

This course will cover the following topics:

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 Interpretability
  • Using Model Explainers

Module 10: Monitoring Models 

  • Monitoring Models