DP-100 Designing and Implementing a Data Science Solution on Azure

TEMATYKA ZAJĘĆ

• Module 1: Getting Started with Azure Machine Learning
  Introduction to Azure Machine Learning
  Working with Azure Machine Learning
  Lab: Create an Azure Machine Learning Workspace

• Module 2: Visual Tools for Machine Learning
  Automated Machine Learning
  Azure Machine Learning Designer
  Lab: Use Automated Machine Learning
  Lab: Use Azure Machine Learning Designer

• Module 3: Running Experiments and Training Models
  Introduction to Experiments
  Training and Registering Models
  Lab: Train Models
  Lab: Run Experiments
  

• Module 4: Working with Data
  Working with Datastores
  Working with Datasets
  Lab: Work with Data
  

• Module 5: Working with Compute
  Working with Environments
  Working with Compute Targets
  Lab: Work with Compute

• Module 6: Orchestrating Operations with Pipelines
  Introduction to Pipelines
  Publishing and Running Pipelines
  Lab: Create a Pipeline
  

• Module 7: Deploying and Consuming Models
  Real-time Inferencing
  Batch Inferencing
  Continuous Integration and Delivery
  Lab: Create a Real-time Inferencing Service
  Lab: Create a Batch Inferencing Service
  

• Module 8: Training Optimal Models
  Hyperparameter Tuning
  Automated Machine Learning
  Lab: Use Automated Machine Learning from the SDK
  Lab: Tune Hyperparameters

• Module 9: Responsible Machine Learning
  Differential Privacy
  Model Interpretability
  Fairness
  Lab: Explore Differential provacy
  Lab: Interpret Models
  Lab: Detect and Mitigate Unfairness

• Module 10: Monitoring Models
  Monitoring Models with Application Insights
  Monitoring Data Drift
  Lab: Monitor Data Drift
  Lab: Monitor a Model with Application Insights