AI + Machine Learning, Azure Machine Learning, Best practices, Developer Tools, SDKs
Automate MLOps workflows with Azure Machine Learning service CLI
By Shivani Patel Program Manager, Azure Machine Learning
1 min read
This blog was co-authored by Jordan Edwards, Senior Program Manager, Azure Machine Learning
This year at Microsoft Build 2019, we announced a slew of new releases as part of Azure Machine Learning service which focused on MLOps. These capabilities help you automate and manage the end-to-end machine learning lifecycle.
Historically, Azure Machine Learning service’s management plane has been via its Python SDK. To make our service more accessible to IT and app development customers unfamiliar with Python, we have delivered an extension to the Azure CLI focused on interacting with Azure Machine Learning.
While it’s not a replacement for the Azure Machine Learning service Python SDK, it is a complimentary tool that is optimized to handle highly parameterized tasks which suit themselves well to automation. With this new CLI, you can easily perform a variety of automated tasks against the machine learning workspace, including:
- Datastore management
- Compute target management
- Experiment submission and job management
- Model registration and deployment
Combining these commands enables you to train, register their model, package it, and deploy your model as an API. To help you quickly get started with MLOps, we have also released a predefined template in Azure Pipelines. This template allows you to easily train, register, and deploy your machine learning models. Data scientists and developers can work together to build a custom application for their scenario built from their own data set.
The Azure Machine Learning service Command-Line Interface is an extension to the interface for the Azure platform. This extension provides commands for working with Azure Machine Learning service from the command-line and allows you to automate your machine learning workflows. Some key scenarios would include:
- Running experiments to create machine learning models
- Registering machine learning models for customer usage
- Packaging, deploying, and tracking the lifecycle of machine learning models
To use the Azure Machine Learning CLI, you must have an Azure subscription. If you don’t have an Azure subscription, you can create a free account before you begin. Try the free or paid version of Azure Machine Learning service to get started today.
Learn more about the Azure Machine Learning service.
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