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DP 100: Designing and Implementing a Data Science Solution on Azure
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Lesson 1: Design A Machine Learning Solution
1.1.1 Introduction to Design a Data Ingestion Strategy For Machine Learning Projects
1.1.2 Identify Your Data Source And Format
1.1.3 Choose How to Serve Data to Machine Learning Workflows = Serving Data to Workflows
1.1.4 Design a Data Ingestion Solution
1.1.5 Summary
1.2.1 Introduction to Design A Machine Learning Model Training Solution
1.2.2 Identify Machine Learning Tasks
1.2.3 Choose a Service To Train a Model
1.2.4 Choose Between Compute Options
1.2.5 Summary
1.3.1 Introduction to Design a Model Deployment Solution
1.3.2 Understand How a Model Will Be Consumed
1.3.3 Decide On Real-Time or Batch Deployment
1.3.4 Summary
1.4.1 Introduction to Design a Responsible Machine Learning Solution
1.4.2 Explore MLOps Architecture
1.4.3 Design for Monitoring
1.4.4 Design for Retraining
1.4.5 Summary
Lesson 2 : Explore and Configure the Azure Machine Learning Workspace
2.1.1 Introduction to Explore Azure Machine Learning Workspace Resources and Assets
2.1.2 Create an Azure Machine Learning Workspace
2.1.3 Identify Azure Machine Learning Resources
2.1.4 Identify Azure Machine Learning Assets
2.1.5 Train Models in the Workspace
2.1.6 Summary
2.2.1 Introduction to Explore Developer Tools for Workspace Interaction
2.2.2 Explore Azure Machine Learning Studio
2.2.3 Explore Python SDK for Azure ML
2.2.4 Explore the CLI
2.2.5 Summary
2.3.1 Introduction to Make Data Available in Azure Machine Learning
2.3.2 Understand URL
2.3.3 Create a Datastore
2.3.4 Create a Data Asset
2.3.5 Summary
2.4.1 Introduction to Work with compute targets in Azure Machine Learning
2.4.2 Choose the Appropriate
2.4.3 Creating and Using a Compute Instance
2.4.4 Creating and Using a Compute Cluster
2.4.5 Summary
2.5.1 Introduction to Work With Environments in Azure Machine Learning
2.5.2 Understand Environments
2.5.3 Explore and Use Curated Environments
2.5.4 Create and Use Custom Environments
2.5.5 Summary
Lesson 3 : Experiment with Azure Machine Learnings
3.1.1 Introduction to Find The Best Classification Model With Automated Machine Learning
3.1.2 Prepare Data for AutoML
3.1.3 Configure and Run an AutoML Experiment
3.1.4 Evaluate and Compare Models
3.1.5 Summary
3.2.1 Introduction to Track Model Training in Jupyter Notebooks with MLflow
3.2.2 Configure MLflow for Modwl Tracking in Notebooks
3.2.3 Train and Track Models in Notebooks
3.2.4 Summary
Lesson 4: Optimize Model Training with Azure Machine Learning
4.1.1 Introduction to Run a Training Script as a Command Job in Azure Machine Learning
4.1.2 Convert a Notebook to a Script
4.1.3 Run a Script as a Command Job
4.1.4 Use Parameters In A Command Job
4.1.5 Summary
4.2.1 Introduction to Track Model Training With MLflow in Jobs
4.2.2 Track Matrics with MLflow
4.2.3 View Matrics and Evaluate Models
4.2.4 Summary
4.3.1 Introduction to Hyperparameters in ML
4.3.2 Defining a Search Space
4.3.3 Configuring Sampling Methods
4.3.4 Configure Early Termination
4.3.5 Use a Sweep Job for Hyperparameter Tuning
4.3.6 Summary
4.4.1 Introduction to Run pipelines in Azure Machine Learning
4.4.2 Create Component
4.4.3 Create a Pipeline
4.4.4 Run a Pipeline Job
4.4.5 Summary
Lesson 5: Manage and Review Models in Azure Machine Learning
5.1.1 Introduction to Model Deployment in Azure ML
5.1.2 Log Models with MLflow
5.1.3 Understand the MLflow Model Format
5.1.4 Register an MLflow Model
5.1.5 Summary
5.2.1 Introduction to Create and explore the Responsible AI dashboard for a model in Azure Machine Learning
5.2.2 Understand Responsible AI Concepts
5.2.3 Create a Responsible AI Dashboard
5.2.4 Evaluate the Responsible AI Dashboard
5.2.5 Summary
Lesson 6: Deploy and Consume Models with Azure Machine Learning
6.1.1 Introduction to Deploy a Model to a Managed Online Endpoint
6.1.2 Explore Managed Online Endpoints
6.1.3 Deploy Your MLflow Model to a Managed Online Endpoint
6.1.4 Deploy a Model to a Managed Online Endpoint
6.1.5 Test Managed Online Endpoints
6.1.6 Summary
6.2.1 Introduction to Deploy a Model to a Batch Endpoint
6.2.2 Understanding and Creating Batch Endpoints
6.2.3 Deploying Your MLflow Model to a Batch Endpoint
6.2.4 Deploy a Custom Model to a Batch Endpoint
6.2.5 Invoke and Troubleshoot Batch Endpoints
6.2.6 Summary
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DP 100: Designing and Implementing a Data Science Solution on Azure
