Explore best practices for DP-100: Designing and implementing a data science solution on Azure learning path 10

Completed

DP-100: Learning path 10 Deploy and consuming models with Azure Machine Learning.

Learning path 10 overview

As you begin to teach this learning path, get familiar with what the students learn during the learning path. In this learning path student, build on their learning about how models are designed to help decision-making through predictions and learn how to deploy the models both in real time inferencing and batch inferencing.

This learning path consists of three focus areas:

• Deploy a model to a managed online endpoint
• Deploy a model to a batch endpoint
• Explain what is new in the concepts of endpoint

Learning path 10 tips and tricks

• Highlight examples for each scenario
• Describe the pros and cons for each approach
• Explain how the representational state transfer (REST) API works and where students might have seen it previously

Learning path 10 demos (optional)

• Inference pipeline
• Iterative process structure
• Endpoint Health status

Learning path 10 labs

• Lab 10.1: Log and register models with MLflow
• Lab 10.2: Deploy a model to a batch endpoint