Designing And Implementing A Data Science Solution On Azure in Washington D.C.

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