Designing and Implementing a Data Science Solution on Azure DP100 Course Outline
Module 1: Design a Data Ingestion Strategy for Machine Learning Projects
- Introduction
- Identify Your Data Source and Format
- Choose How to Serve Data to Machine Learning Workflows
- Design a Data Ingestion Solution
- Exercise: Design a Data Ingestion Strategy
Module 2: Design a Machine Learning Model Training Solution
- Introduction
- Identify Machine Learning Tasks
- Choose a Service to Train a Machine Learning Model
- Decide Between Compute Options
- Exercise: Design a Model Training Strategy
Module 3: Design a Model Deployment Solution
- Introduction
- Understand How Model Will Be Consumed
- Decide on Real-Time or Batch Deployment
- Exercise - Design a Deployment Solution
Module 4: Azure Machine Learning Workspace Resources and Assets
- Introduction
- Video - Explore the Azure Machine Learning Workspace
- Create an Azure Machine Learning Workspace
- Identify Azure Machine Learning Resources
- Identify Azure Machine Learning Assets
- Train Models in the Workspace
- Exercise - Explore the Workspace
Module 5: Developer Tools for Workspace Interaction
- Introduction
- Studio
- Python SDK
- CLI
- Exercise-Explore the Developer Tools
Module 6: Make Data Available in Azure Machine Learning
- Introduction
- Video - Make Data Available in Azure Machine Learning
- Understand URIs
- Create a Datastore
- Create a Data Asset
- Exercise - Make Data Available in Azure Machine Learning
Module 7: Work with Compute Targets in Azure Machine Learning
- Introduction
- Choose the Appropriate Compute Target
- Create and Use a Compute Instance
- Create and Use a Compute Cluster
- Exercise - Work with Compute Resources
Module 8: Work with Environments in Azure Machine Learning
- Introduction
- Understand Environments
- Use Curated Environments
- Create and Use Custom Environments
- Exercise - Work with Environments
Module 9: Classification Model with Automated Machine Learning
- Introduction
- Video - Find the Best Classification Model with Automated Machine Learning
- Preprocess Data and Configure Featurisation
- Run an Automated Machine Learning Experiment
- Evaluate and Compare Models
- Exercise - Find the Best Classification Model with Automated Machine Learning
Module 10: Track Model Training in Jupyter Notebooks with MLflow
- Introduction
- Configure MLflow For Model Tracking in Notebooks
- Train and Track Models in Notebooks
- Exercise - Track Model Training
Module 11: Run Training Script as a Command Job in Azure Machine Learning
- Introduction
- Video - Run a Training Script as a Command Job in Azure Machine Learning
- Convert a Notebook to a Script
- Run a Script as a Command Job
- Use Parameters in a Command Job
- Exercise - Run a Training Script as a Command Job
Module 12: Track Model Training with MLflow in Jobs
- Introduction
- Video - Track Model Training with MLFlow in Jobs
- Track Metrics with MLflow
- View Metrics and Evaluate Models
- Exercise - Use MLflow to Track Training Jobs
Module 13: Run Pipelines in Azure Machine Learning
- Introduction
- Video - Run Pipelines in Azure Machine Learning
- Create Components
- Create a Pipeline
- Run a Pipeline Job
- Exercise - Run a Pipeline Job
Module 14: Perform Hyperparameter Tuning with Azure Machine Learning
- Introduction
- Define a Search Space
- Configure a Sampling Method
- Configure Early Termination
- Use a Sweep Job for Hyperparameter Tuning
- Exercise - Run a Sweep Job
Module 15: Deploy a Model to a Managed Online Endpoint
- Introduction
- Managed Online Endpoints
- Deploy the MLflow Model to a Managed Online Endpoint
- Deploy a Model to a Managed Online Endpoint
- Test Managed Online Endpoints
- Exercise - Deploy an MLflow Model to an Online Endpoint
Module 16: Deploy a Model to a Batch Endpoint
- Introduction
- Video - Deploy a Model to a Batch Endpoint
- Understand and Create Batch Endpoints
- Deploy Your MLflow Model to a Batch Endpoint
- Deploy a Custom Model to a Batch Endpoint
- Invoke and Troubleshoot Batch Endpoints
- Exercise - Deploy an MLflow Model to a Batch Endpoint