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

Design a Data Ingestion Strategy for Machine Learning Projects

• Identify your data source and format
• Choose how to serve data to machine learning workflows
• Design a data ingestion solution
• Lab: Design a data ingestion strategy

Design a Machine Learning Model Training Solution

• Identify machine learning tasks
• Choose a service to train a model
• Choose between compute options
• Lab: Design a model training strategy

Design a Model Deployment Solution

• Understand how a model will be consumed
• Decide whether to deploy your model to a real-time or batch endpoint
• Lab: Design a deployment solution

Explore Azure Machine Learning Workspace Resources and Assets

• Create an Azure Machine Learning workspace
• Identify resources and assets
• Train models in the workspace
• Lab: Explore the workspace

Explore Developer Tools for Workspace Interaction

• The Azure Machine Learning studio
• The Python Software Development Kit (SDK)
• The Azure Command Line Interface (CLI)
• Lab: Explore the developer tools

Make Data Available in Azure Machine Learning

• Work with Uniform Resource Identifiers (URIs)
• Create and use datastores
• Create and use data assets
• Lab: Make data available

Work with Compute Targets in Azure Machine Learning

• Choose the appropriate compute target
• Create and use a compute instance
• Create and use a compute cluster
• Lab: Work with compute resources

Work with Environments in Azure Machine Learning

• Understand environments in Azure Machine Learning
• Explore and use curated environments
• Create and use custom environments
• Lab: Work with environments
  

Find the Best Classification Model with Automated Machine Learning

• Prepare your data to use AutoML for classification
• Configure and run an AutoML experiment
• Evaluate and compare models
• Lab: Find the best classification model

Track Model Training in Jupyter Notebooks with MLflow

• Configure to use MLflow in notebooks
• Use MLflow for model tracking in notebooks
• Lab: Track model training

Run a Training Script as a Command Job in Azure Machine Learning

• Convert a notebook to a script
• Test scripts in a terminal
• Run a script as a command job
• Use parameters in a command job
• Lab: Run a training script as a command job

Track Model Training with MLflow in Jobs

• Use MLflow when you run a script as a job
• Review metrics, parameters, artifacts, and models from a run
• Lab: Use MLflow to track training jobs

Run Pipelines in Azure Machine Learning

• Create components
• Build an Azure Machine Learning pipeline
• Run an Azure Machine Learning pipeline
• Lab: Run a pipeline job

Perform Hyperparameter Tuning with Azure Machine Learning

• Define a hyperparameter search space
• Configure hyperparameter sampling
• Select an early-termination policy
• Run a sweep job
• Lab: Run a sweep job

Deploy a Model to a Managed Online Endpoint

• Use managed online endpoints
• Deploy your MLflow model to a managed online endpoint
• Deploy a custom model to a managed online endpoint
• Test online endpoints
• Lab: Deploy an MLflow model to an online endpoint

Deploy a Model to a Batch Endpoint

• Create a batch endpoint
• Deploy your MLflow model to a batch endpoint
• Deploy a custom model to a batch endpoint
• Invoke batch endpoints
• Lab: Deploy an MLflow model to a batch endpoint