Azure Machine Learning includes several resources and assets to enable you to perform your machine learning tasks. These resources and assets are needed to run any job.
Resources: setup or infrastructural resources needed to run a machine learning workflow. Resources include:
Assets: created using Azure Machine Learning commands or as part of a training/scoring run. Assets are versioned and can be registered in the Azure Machine Learning workspace. They include:
Create a connection to your Azure Machine Learning subscription. The examples all rely on ml_client. To create a workspace, the connection doesn't need a workspace name, since you may not yet have one. All other examples in this article require that the workspace name is included in the connection.
# import required libraries
from azure.ai.ml import MLClient
from azure.ai.ml.entities import Workspace
from azure.identity import DefaultAzureCredential
# Enter details of your subscription
subscription_id = "<SUBSCRIPTION_ID>"
resource_group = "<RESOURCE_GROUP>"
# get a handle to the subscription (use this if you haven't created a workspace yet)
ml_client = MLClient(DefaultAzureCredential(), subscription_id, resource_group)
# all other examples in this article require the connection to include workspace name
workspace_name = "<WORKSPACE_NAME>"
ml_client = ml_client = MLClient(DefaultAzureCredential(), subscription_id, resource_group, workspace_name)
To use the Azure CLI code examples in this article, you need to have the Azure CLI installed and configured. You can install the Azure CLI from the Install and set up the CLI (v2).
Once you have the Azure CLI installed, sign in to your Azure account:
az login
If you have access to multiple Azure subscriptions, you set your active subscription:
az account set -s "<YOUR_SUBSCRIPTION_NAME_OR_ID>"
The workspace is the top-level resource for Azure Machine Learning, providing a centralized place to work with all the artifacts you create when you use Azure Machine Learning. The workspace keeps a history of all jobs, including logs, metrics, output, and a snapshot of your scripts. The workspace stores references to resources like datastores and compute. It also holds all assets like models, environments, components, and data asset.
# specify the workspace details
ws = Workspace(
name="my_workspace",
location="eastus",
display_name="My workspace",
description="This example shows how to create a workspace",
tags=dict(purpose="demo"),
)
ml_client.workspaces.begin_create(ws) # use MLClient to connect to the subscription and resource group and create workspace
This Jupyter notebook shows more ways to create an Azure Machine Learning workspace using SDK v2.
To create a workspace using CLI v2, use the following command:
A compute is a designated compute resource where you run your job or host your endpoint. Azure Machine Learning supports the following types of compute:
Compute instance - a fully configured and managed development environment in the cloud. You can use the instance as a training or inference compute for development and testing. It's similar to a virtual machine on the cloud.
Compute cluster - a managed-compute infrastructure that allows you to easily create a cluster of CPU or GPU compute nodes in the cloud.
Serverless compute - a compute cluster you access on the fly. When you use serverless compute, you don't need to create your own cluster. All compute lifecycle management is offloaded to Azure Machine Learning.
Inference cluster - used to deploy trained machine learning models to Azure Kubernetes Service. You can create an Azure Kubernetes Service (AKS) cluster from your Azure Machine Learning workspace, or attach an existing AKS cluster.
Attached compute - You can attach your own compute resources to your workspace and use them for training and inference.
Azure Machine Learning datastores securely keep the connection information to your data storage on Azure, so you don't have to code it in your scripts. You can register and create a datastore to easily connect to your storage account, and access the data in your underlying storage service. The CLI v2 and SDK v2 support the following types of cloud-based storage services:
Azure Machine Learning models consist of one or more binary files that represent a machine learning model and any corresponding metadata. Models can be created from a local or remote file or directory. For remote locations https, wasbs and azureml locations are supported. The created model is tracked in the workspace under the specified name and version. Azure Machine Learning supports three types of storage format for models:
custom_model
mlflow_model
triton_model
Create a model in the model registry
Model registration allows you to store and version your models in the Azure cloud, in your workspace. The model registry helps you organize and keep track of your trained models.
Azure Machine Learning environments are an encapsulation of the environment where your machine learning task happens. They specify the software packages, environment variables, and software settings around your training and scoring scripts. The environments are managed and versioned entities within your Machine Learning workspace. Environments enable reproducible, auditable, and portable machine learning workflows across various computes.
Types of environment
Azure Machine Learning supports two types of environments: curated and custom.
Curated environments are provided by Azure Machine Learning and are available in your workspace by default. Intended to be used as is, they contain collections of Python packages and settings to help you get started with various machine learning frameworks. These precreated environments also allow for faster deployment time. For a full list, see the curated environments article.
In custom environments, you're responsible for setting up your environment and installing packages or any other dependencies that your training or scoring script needs on the compute. Azure Machine Learning allows you to create your own environment using
A docker image
A base docker image with a conda YAML to customize further
A docker build context
Create an Azure Machine Learning custom environment
Azure Machine Learning allows you to work with different types of data:
URIs (a location in local/cloud storage)
uri_folder
uri_file
Tables (a tabular data abstraction)
mltable
Primitives
string
boolean
number
For most scenarios, you use URIs (uri_folder and uri_file) - a location in storage that can be easily mapped to the filesystem of a compute node in a job by either mounting or downloading the storage to the node.
mltable is an abstraction for tabular data that is to be used for AutoML Jobs, Parallel Jobs, and some advanced scenarios. If you're just starting to use Azure Machine Learning and aren't using AutoML, we strongly encourage you to begin with URIs.
Component
An Azure Machine Learning component is a self-contained piece of code that does one step in a machine learning pipeline. Components are the building blocks of advanced machine learning pipelines. Components can do tasks such as data processing, model training, model scoring, and so on. A component is analogous to a function - it has a name, parameters, expects input, and returns output.
Manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Python, Azure Machine Learning and MLflow.