Deploy and consume models with Azure Machine Learning
Offered By: Microsoft via Microsoft Learn
Course Description
Overview
- Module 1: Learn how to deploy models to a managed online endpoint for real-time inferencing.
In this module, you'll learn how to:
- 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.
- Module 2: Azure Machine Learning Python SDK v2.
In this module, you'll learn how to:
- Create a batch endpoint.
- Deploy your MLflow model to a batch endpoint.
- Deploy a custom model to a batch endpoint.
- Invoke batch endpoints.
Syllabus
- Module 1: Module 1: Deploy a model to a managed online endpoint
- Introduction
- Explore managed online endpoints
- Deploy your 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
- Knowledge check
- Summary
- Module 2: Module 2: Deploy a model to a batch endpoint
- Introduction
- 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
- Knowledge check
- Summary
Tags
Related Courses
Advanced Deployment Scenarios with TensorFlowDeepLearning.AI via Coursera Data Pipelines with TensorFlow Data Services
DeepLearning.AI via Coursera Device-based Models with TensorFlow Lite
DeepLearning.AI via Coursera Preparing for the Google Cloud Professional Data Engineer Exam 日本語版
Google Cloud via Coursera Preparing for the Google Cloud Professional Data Engineer Exam en Español
Google Cloud via Coursera