Deploying and Managing Models in Microsoft Azure
To deliver a great idea, one needs to deploy your ML models as well as ensure they keep up to date. This course will teach you the best practices to deploy, manage, and retrain machine learning models in Azure.
What you'll learn
Once a model is created, we need to deploy it. Moreover, we need to ensure their performance does not decay and we keep track of the current model in production. In this course, Deploying and Managing Models in Microsoft Azure, you’ll learn the best practices to deploy, manage, and retrain machine learning models in Azure. First, you’ll explore how to deploy machine learning models in Azure. Next, you’ll discover how to create a retraining pipeline with Azure ML Pipelines. Finally, you’ll learn about MLOps practices to take into account. When you’re finished with this course, you’ll have the skills and knowledge of machine learning operations in Azure needed to deploy, manage, and retrain machine learning models in Azure.
Table of contents
- Deployment Options in Azure Machine Learning 4m
- Real-time and Batch Endpoints 4m
- Demo: Deploying Locally - Part 1 3m
- Demo: Deploying Locally - Part 2 8m
- Demo: Deploying Locally - Part 3 1m
- Demo: Deploy an Online Endpoint 5m
- Demo: Perform A/B Testing in the Studio - Part 1 3m
- Demo: Perform A/B Testing in the Studio - Part 2 5m
- Demo: Deploying a Real-time A/B Test to Kubernetes - Part 1 4m
- Demo: Deploying a Real-time A/B Test to Kubernetes - Part 2 5m
- Key Takeaways and Tips 2m
- CI/CD in Machine Learning 6m
- Different Types of Pipelines in Azure 5m
- Demo: Creating an Azure Pipelines Project - Part1 8m
- Demo: Creating an Azure Pipelines Project - Part2 5m
- Demo: Creating an Azure ML Pipeline - Part 1 6m
- Demo: Creating an Azure ML Pipeline - Part 2 5m
- Demo: Creating an Azure ML Pipeline - Part 3 3m
- Key Takeaways and Tips 2m