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Discover your path. Whether you're just starting or an experienced professional, our hands-on approach helps you arrive at your goals faster, with more confidence and at your own pace. Master core concepts at your speed and on your schedule. Whether you've got 15 minutes or an hour, you can develop practical skills through interactive modules

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Azure on Microsoft Learn Microsoft Docs

Continue your learning journey with Microsoft Virtual Training Days that provide free, instructor-led, technical skilling in multiple languages and time zones across a variety of topics. Browse training events. Your path to get started. x of y. x of y. x of y. x of y. x of y. x of y. Popular learning paths and modules.

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How to use the learning hub Microsoft Docs

In this article. [This topic is pre-release documentation and is subject to change.] With the learning hub, you can explore documents, videos, and other resources that will help make it easier for you to create the website you want. To access the learning hub, go to the Power Pages home page and select Learn on the left pane.

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Enroll as a Learning Partner

To activate your Learning Action Pack, use the following steps: Buy or renew a Microsoft Action Pack subscription. Contact the Partner Frontline Support team to help you set up a new account profile. Doing so gives you access to the Microsoft Courseware training in the Courseware Marketplace.

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How Azure Machine Learning works (v2)

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.

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Machine Learning CLI (v1)

The Azure CLI commands in articles in this section require the azure-cli-ml, or v1, extension for Azure Machine Learning. The enhanced v2 CLI using the ml extension is now available and recommended. The extensions are incompatible, so v2 CLI commands will not work for articles in this directory. However, machine learning workspaces and all

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CLI & SDK v2

The Azure Machine Learning CLI v2 (CLI v2) is the latest extension for the Azure CLI. The CLI v2 provides commands in the format az ml <noun> <verb> <options> to create and maintain Azure ML assets and workflows. The assets or workflows themselves are defined using a YAML file. The YAML file defines the configuration of the asset or workflow

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Architecture & key concepts (v1)

A machine learning workspace is the top-level resource for Azure Machine Learning. The workspace is the centralized place to: Manage resources you use for training and deployment of models, such as computes. Store assets you create when you use Azure Machine Learning, including: Environments. Experiments.

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Secure data access in the cloud

Azure Machine Learning gives you a central location to create, manage, and monitor labeling projects. Labeling projects help coordinate the data, labels, and team members, allowing you to more efficiently manage the labeling tasks. Currently supported tasks are image classification, either multi-label or multi-class, and object identification

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Free courses from LinkedIn Learning Microsoft Docs

In this article. A selection of courses are available from LinkedIn Learning for free. This means you don't need an Enterprise license for users in your organization to access these courses through Viva Learning. LinkedIn Learning is enabled as a content source by default in Viva Learning. Learn more about content sources in Viva Learning.

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Work with data using SDK v2 (preview)

Azure Machine Learning allows you to work with different types of data. In this article, you'll learn about using the Python SDK v2 to work with URIs and Tables. URIs reference a location either local to your development environment or in the cloud. Tables are a tabular data abstraction. For most scenarios, you'll use URIs (uri_folder and uri

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Make data-driven policies and influence decision making

By incorporating individual machine learning steps into interpretable causal models, these methods improve the reliability of what-if predictions and make causal analysis quicker and easier for a broad set of users. DoWhy is a Python library that aims to spark causal thinking and analysis. DoWhy provides a principled four-step interface for

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Create and run machine learning pipelines using components with …

In this article. APPLIES TO: Python SDK azure-ai-ml v2 (preview). In this article, you learn how to build an Azure Machine Learning pipeline using Python SDK v2 to complete an image classification task containing three steps: prepare data, train an image classification model, and score the model. Machine learning pipelines optimize your workflow with speed, …

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Create and run component-based ML pipelines (UI)

Register the components to AzureML workspace using following commands. Learn more about ML components. az ml component create --file train.yml az ml component create --file score.yml az ml component create --file eval.yml. After register component successfully, you can see your component in the studio UI.

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Train models with the Azure ML Python SDK v2 (preview)

If you don't have an Azure subscription, create a free account before you begin. Try the free or paid version of Azure Machine Learning today; The Azure Machine Learning SDK v2 for Python; An Azure Machine Learning workspace; Clone examples repository. To run the training examples, first clone the examples repository and change into the sdk

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MLOps: ML model management v1

Machine Learning pipelines allow you to define repeatable and reusable steps for your data preparation, training, and scoring processes. Create reusable software environments for training and deploying models. Register, package, and deploy models from anywhere. You can also track associated metadata required to use the model.

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Assess AI systems and make data-driven decisions with Azure …

Who should use the Responsible AI dashboard? The Responsible AI dashboard, and its corresponding Responsible AI scorecard, could be incorporated by the following personas to build trust with AI systems.. Machine learning model engineers and data scientists who are interested in debugging and improving their machine learning models pre-deployment.

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Azure Machine Learning anywhere with Kubernetes (preview)

Azure Machine Learning workspace defaults to having a system-assigned managed identity to access Azure ML resources. The steps are completed if the system assigned default setting is on. Otherwise, if a user-assigned managed identity is specified in Azure Machine Learning workspace creation, the following role assignments need to be granted to

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Authenticate to an online endpoint

Configure the endpoint authentication. You can set the authentication type when you create an online endpoint. Set the auth_mode to key or aml_token depending on which one you want to use. The default value is key. When deploying using CLI v2, set this value in the online endpoint YAML file. For more information, see How to deploy an online

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Set up AutoML with Python

In this guide, learn how to set up an automated machine learning, AutoML, training run with the Azure Machine Learning Python SDK using Azure Machine Learning automated ML. Automated ML picks an algorithm and hyperparameters for you and generates a model ready for deployment. This guide provides details of the various options that you can …

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Create Azure Machine Learning datasets

An Azure Machine Learning workspace. The Azure Machine Learning SDK for Python installed, which includes the azureml-datasets package. Create an Azure Machine Learning compute instance, which is a fully configured and managed development environment that includes integrated notebooks and the SDK already installed. OR

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Assess errors in ML models

Model accuracy may not be uniform across subgroups of data, and there might exist input cohorts for which the model fails more often. The direct consequences of these failures are a lack of reliability and safety, unfairness, and a loss of trust in machine learning altogether.

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How to use studio UI to build and debug Machine Learning …

The system_logs folder contains logs generated by Azure Machine Learning. Learn more about how to view and download log files for a run . If you don't see those folders, this is due to the compute run time update isn't released to the compute cluster yet, and you can look at 70_driver_log.txt under azureml-logs folder first.

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Machine Learning pipeline YAML (v1)

The YAML syntax detailed in this document is based on the JSON schema for the v1 version of the ML CLI extension. This syntax is guaranteed only to work with the ML CLI v1 extension. Switch to the v2 (current version) for the syntax for ML CLI v2. Define your machine learning pipelines in YAML. When using the machine learning extension for the

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Log & view metrics and log files v1

View and download log files for a run. Log files are an essential resource for debugging the Azure ML workloads. After submitting a training job, drill down to a specific run to view its logs and outputs: Navigate to the Experiments tab. Select the runID for a specific run. Select Outputs and logs at the top of the page.

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Add learning management systems for Microsoft Viva Learning

A growing set of learning management systems are available through Viva Learning. This set may change at any time as more providers join or change their status with the program. Learning management systems are not enabled by default. To enable these sources, you will need to add them in the Microsoft 365 admin center and follow the specific

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Generate Responsible AI dashboard in the studio UI (preview)

In this article. You can create a Responsible AI dashboard with a no-code experience in the Azure Machine Learning studio UI. To start the wizard, navigate to the registered model you’d like to create Responsible AI insights for and select the Details tab. Then select the Create Responsible AI dashboard (preview) button.. The wizard is designed to …

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Network isolation of managed online endpoints

Verify that the client issuing the scoring request is a virtual network that can access the Azure Machine Learning workspace. Use the nslookup command on the endpoint hostname to retrieve the IP address information: Bash. nslookup endpointname.westcentralus.inference.ml.azure.com. The response contains an address.

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Counterfactuals analysis and what-if

The counterfactual analysis component enables you to identify which features to vary and their permissible ranges for valid and logical counterfactual examples. Use What-If Counterfactuals when you need to: Examine fairness and reliability criteria as a decision evaluator (by perturbing sensitive attributes such as gender, ethnicity, etc., and

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Generate Responsible AI dashboard with YAML and Python …

Once your Responsible AI dashboard is generated, view how to access and use it in Azure Machine Learning studio; Summarize and share your Responsible AI insights with the Responsible AI scorecard as a PDF export. Learn more about the concepts and techniques behind the Responsible AI dashboard. Learn more about how to collect data responsibly

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Scaling responsible MLOps with Azure Machine Learning

Accelerate Machine Learning workflows while applying responsible AI throughout the Machine Learning lifecycle. Related Build Sessions. The resources on this page were selected to help you learn more about the topics and services covered in these sessions:

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MLflow Tracking for models

MLflow is an open-source library for managing the life cycle of your machine learning experiments. MLflow Tracking is a component of MLflow that logs and tracks your training run metrics and model artifacts, no matter your experiment's environment--locally on your computer, on a remote compute target, a virtual machine, or an Azure Databricks

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Install and set up the CLI (v1) Microsoft Docs

The Azure Machine Learning CLI is an extension to the Azure CLI, a cross-platform command-line interface for the Azure platform. This extension provides commands for working with Azure Machine Learning. It allows you to automate your machine learning activities. The following list provides some example actions that you can do with the CLI

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Deploy ML models to Kubernetes Service with v1

Prerequisites. An Azure Machine Learning workspace. For more information, see Create an Azure Machine Learning workspace.. A machine learning model registered in your workspace. If you don't have a registered model, see How and where to deploy models.. The Azure CLI extension (v1) for Machine Learning service, Azure Machine Learning Python …

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How to use the Responsible AI dashboard in studio (preview)

The Responsible AI dashboard includes a robust and rich set of visualizations and functionality to help you analyze your machine learning model or making data-driven business decisions: Global controls

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