Merge branch 'main' of https://github.com/microsoftdocs/azure-ai-docs-pr into onnx

Author: v-thepet

articles/ai-services/content-safety/quickstart-groundedness.md

@@ -39,7 +39,7 @@ This section walks through a sample request with cURL. Paste the command below i
 
 
     ```shell
-    curl --location --request POST '<endpoint>/contentsafety/text:detectGroundedness?api-version=2024-02-15-preview' \
+    curl --location --request POST '<endpoint>/contentsafety/text:detectGroundedness?api-version=2024-09-15-preview' \
     --header 'Ocp-Apim-Subscription-Key: <your_subscription_key>' \
     --header 'Content-Type: application/json' \
     --data-raw '{
@@ -87,7 +87,7 @@ Create a new Python file named _quickstart.py_. Open the new file in your prefer
       'Ocp-Apim-Subscription-Key': '<your_subscription_key>',
       'Content-Type': 'application/json'
     }
-    conn.request("POST", "/contentsafety/text:detectGroundedness?api-version=2024-02-15-preview", payload, headers)
+    conn.request("POST", "/contentsafety/text:detectGroundedness?api-version=2024-09-15-preview", payload, headers)
     res = conn.getresponse()
     data = res.read()
     print(data.decode("utf-8"))
@@ -124,7 +124,7 @@ The following fields must be included in the URL:
 
 | Name     | Required | Description | Type   |
 | :-------------- | :-------- | :------ | :----- |
-| **API Version** | Required  | This is the API version to be used. The current version is: api-version=2024-02-15-preview. Example: `<endpoint>/contentsafety/text:detectGroundedness?api-version=2024-02-15-preview` | String |
+| **API Version** | Required  | This is the API version to be used. The current version is: api-version=2024-09-15-preview. Example: `<endpoint>/contentsafety/text:detectGroundedness?api-version=2024-09-15-preview` | String |
 
 The parameters in the request body are defined in this table:
 
@@ -210,7 +210,7 @@ This section walks through a sample request with cURL. Paste the command below i
 
 
     ```shell
-    curl --location --request POST '<endpoint>/contentsafety/text:detectGroundedness?api-version=2024-02-15-preview' \
+    curl --location --request POST '<endpoint>/contentsafety/text:detectGroundedness?api-version=2024-09-15-preview' \
     --header 'Ocp-Apim-Subscription-Key: <your_subscription_key>' \
     --header 'Content-Type: application/json' \
     --data-raw '{
@@ -244,7 +244,7 @@ Create a new Python file named _quickstart.py_. Open the new file in your prefer
     import http.client
     import json
     
-    conn = http.client.HTTPSConnection("<endpoint>/contentsafety/text:detectGroundedness?api-version=2024-02-15-preview")
+    conn = http.client.HTTPSConnection("<endpoint>/contentsafety/text:detectGroundedness?api-version=2024-09-15-preview")
     payload = json.dumps({
       "domain": "Generic",
       "task": "QnA",
@@ -266,7 +266,7 @@ Create a new Python file named _quickstart.py_. Open the new file in your prefer
       'Ocp-Apim-Subscription-Key': '<your_subscription_key>',
       'Content-Type': 'application/json'
     }
-    conn.request("POST", "/contentsafety/text:detectGroundedness?api-version=2024-02-15-preview", payload, headers)
+    conn.request("POST", "/contentsafety/text:detectGroundedness?api-version=2024-09-15-preview", payload, headers)
     res = conn.getresponse()
     data = res.read()
     print(data.decode("utf-8"))
@@ -389,7 +389,7 @@ This section demonstrates a sample request using cURL. Replace the placeholders
 
 
 ```shell
-curl --location --request POST '<endpoint>/contentsafety/text:detectGroundedness?api-version=2024-02-15-preview' \
+curl --location --request POST '<endpoint>/contentsafety/text:detectGroundedness?api-version=2024-09-15-preview' \
 --header 'Ocp-Apim-Subscription-Key: <your_subscription_key>' \
 --header 'Content-Type: application/json' \
 --data-raw '{
@@ -412,7 +412,7 @@ curl --location --request POST '<endpoint>/contentsafety/text:detectGroundedness
 Create a Python script named quickstart.py and include the following code. Update the endpoint URL and key as appropriate:
 ```Python
 
-conn = http.client.HTTPSConnection("<endpoint>/contentsafety/text:detectGroundedness?api-version=2024-02-15-preview")
+conn = http.client.HTTPSConnection("<endpoint>/contentsafety/text:detectGroundedness?api-version=2024-09-15-preview")
 payload = json.dumps({
   "domain": "Generic",
   "task": "Summarization",
@@ -431,7 +431,7 @@ headers = {
   'Ocp-Apim-Subscription-Key': '<your_subscription_key>',
   'Content-Type': 'application/json'
 }
-conn.request("POST", "/contentsafety/text:detectGroundedness?api-version=2024-02-15-preview", payload, headers)
+conn.request("POST", "/contentsafety/text:detectGroundedness?api-version=2024-09-15-preview", payload, headers)
 res = conn.getresponse()
 data = res.read()
 print(data.decode("utf-8"))

articles/ai-studio/concepts/deployments-overview.md

@@ -7,7 +7,7 @@ ms.service: azure-ai-studio
 ms.custom:
   - ignite-2023
   - build-2024
-ms.topic: conceptual
+ms.topic: concept-article
 ms.date: 5/21/2024
 ms.reviewer: fasantia
 ms.author: mopeakande
@@ -96,4 +96,4 @@ Optimizing LLMs requires a careful consideration of several factors, including o
 - [Deploy Azure OpenAI models with Azure AI Studio](../how-to/deploy-models-openai.md)
 - [Deploy Meta Llama 3.1 models with Azure AI Studio](../how-to/deploy-models-llama.md)
 - [Deploy large language models with Azure AI Studio](../how-to/deploy-models-open.md)
-- [Azure AI Studio FAQ](../faq.yml)
+- [Model catalog and collections in Azure AI Studio](../how-to/model-catalog-overview.md)

articles/machine-learning/concept-azure-machine-learning-v2.md

@@ -10,7 +10,7 @@ ms.topic: conceptual
 ms.author: sgilley
 author: sdgilley
 ms.reviewer: balapv
-ms.date: 08/21/2024
+ms.date: 09/30/2024
 #Customer intent: As a data scientist, I want to understand the big picture about how Azure Machine Learning works.
 ---
 
@@ -41,7 +41,7 @@ This document provides a quick overview of these resources and assets.
 To use the Python SDK code examples in this article:
 
 1. Install the [Python SDK v2](https://aka.ms/sdk-v2-install)
-2. Create a connection to your Azure Machine Learning subscription.  The examples all rely on `ml_client`. To create a workspace, the connection does not 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.
+2. 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.
 
     ```python
     # import required libraries
@@ -82,7 +82,7 @@ Sign in to [Azure Machine Learning studio](https://ml.azure.com).
 
 ## Workspace
 
-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.
+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.
 
 ### Create a workspace
 
@@ -133,7 +133,7 @@ A compute is a designated compute resource where you run your job or host your e
 
 * **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.
+* **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.
 
@@ -171,11 +171,10 @@ az ml compute create --file my_compute.yml
 ```
 
 For the content of the file, see [compute YAML examples](https://github.com/Azure/azureml-examples/tree/main/cli/resources/compute).
-.
 
 ### [Studio](#tab/azure-studio)
 
-1. Select a workspace if you are not already in one.
+1. Select a workspace if you aren't already in one.
 1. From the left-hand menu, select **Compute**.
 1. On the top, select a tab to specify the type of compute you want to create.
 1. Select **New** to create the new compute.
@@ -235,7 +234,7 @@ For the content of the file, see [datastore YAML examples](https://github.com/Az
 
 ### [Studio](#tab/azure-studio)
 
-1. Select a workspace if you are not already in one.
+1. Select a workspace if you aren't already in one.
 1. From the left-hand menu, select **Data**.
 1. On the top, select **Datastores**.
 1. Select **Create** to create a new datastore.
@@ -246,7 +245,7 @@ To learn more about using a datastore, see [Create and manage data assets](how-t
 
 ## Model
 
-Azure Machine Learning models consist of the binary file(s) 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 will be tracked in the workspace under the specified name and version. Azure Machine Learning supports three types of storage format for models:
+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`
@@ -260,13 +259,13 @@ For more information on how to create models in the registry, see [Work with mod
 
 ## Environment
 
-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 a variety of computes.
+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 pre-created environments also allow for faster deployment time. For a full list, see the [curated environments article](resource-curated-environments.md).
+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](resource-curated-environments.md).
 
 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
 
@@ -291,7 +290,7 @@ For more information, see [environment YAML schema](reference-yaml-environment.m
 
 ### [Studio](#tab/azure-studio)
 
-1. Select a workspace if you are not already in one.
+1. Select a workspace if you aren't already in one.
 1. From the left-hand menu, select **Environments**.
 1. On the top, select **Custom environments**.
 1. Select **Create** to create a new custom environment.
@@ -314,7 +313,7 @@ Azure Machine Learning allows you to work with different types of data:
   * `boolean`
   * `number`
 
-For most scenarios, you'll 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.
+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.
 

articles/machine-learning/concept-mlflow.md

@@ -8,7 +8,7 @@ ms.author: mopeakande
 ms.reviewer: cacrest
 ms.service: azure-machine-learning
 ms.subservice: mlops
-ms.date: 09/25/2024
+ms.date: 09/30/2024
 ms.topic: concept-article
 ms.custom: cliv2, sdkv2, FY25Q1-Linter
 #Customer intent: As a data scientist, I want to understand what MLflow is and does so that I can use MLflow with my models.
@@ -29,11 +29,37 @@ Azure Machine Learning workspaces are MLflow-compatible, which means that you ca
 > [!TIP]
 > Unlike the Azure Machine Learning SDK v1, there's no logging functionality in the Azure Machine Learning v2 SDK. You can use MLflow logging to ensure that your training routines are cloud-agnostic, portable, and have no dependency on Azure Machine Learning.
 
+## What is tracking
+
+When you work with jobs, Azure Machine Learning automatically tracks some information about experiments, such as code, environment, and input and output data. However, models, parameters, and metrics are specific to the scenario, so model builders must configure their tracking.
+
+The saved tracking metadata varies by experiment, and can include:
+
+- Code
+- Environment details such as OS version and Python packages
+- Input data
+- Parameter configurations
+- Models
+- Evaluation metrics 
+- Evaluation visualizations such as confusion matrices and importance plots
+- Evaluation results, including some evaluation predictions
+
+## Benefits of tracking experiments
+
+Whether you train models with jobs in Azure Machine Learning or interactively in notebooks, experiment tracking helps you:
+
+- Organize all of your machine learning experiments in a single place. You can then search and filter experiments and drill down to see details about previous experiments.
+- Easily compare experiments, analyze results, and debug model training.
+- Reproduce or rerun experiments to validate results.
+- Improve collaboration, because you can see what other teammates are doing, share experiment results, and access experiment data programmatically.
+
 ## Tracking with MLflow
 
+Azure Machine Learning workspaces are MLflow-compatible. This compatibility means you can use MLflow to track runs, metrics, parameters, and artifacts in workspaces without needing to change your training routines or inject any cloud-specific syntax. To learn how to use MLflow for tracking experiments and runs in Azure Machine Learning workspaces, see [Track experiments and models with MLflow](how-to-use-mlflow-cli-runs.md).
+
 Azure Machine Learning uses MLflow tracking to log metrics and store artifacts for your experiments. When you're connected to Azure Machine Learning, all MLflow tracking materializes in the workspace you're working in.
 
-To learn how to set up MLflow tracking for experiments and training routines, see [Log metrics, parameters, and files with MLflow](how-to-log-view-metrics.md). You can also [query and compare experiments and runs with MLflow](how-to-track-experiments-mlflow.md).
+To learn how to enable logging to monitor real-time run metrics with MLflow, see [Log metrics, parameters, and files with MLflow](how-to-log-view-metrics.md). You can also [query and compare experiments and runs with MLflow](how-to-track-experiments-mlflow.md).
 
 MLflow in Azure Machine Learning provides a way to centralize tracking. You can connect MLflow to Azure Machine Learning workspaces even when you're working locally or in a different cloud. The Azure Machine Learning workspace provides a centralized, secure, and scalable location to store training metrics and models.
 

articles/machine-learning/concept-v2.md

@@ -9,7 +9,7 @@ ms.topic: conceptual
 ms.author: sgilley
 author: sdgilley
 ms.reviewer: balapv
-ms.date: 01/17/2024
+ms.date: 09/30/2024
 ms.custom: cliv2, sdkv2, devx-track-python
 #Customer intent: As a data scientist, I want to know whether to use v1 or v2 of CLI and SDK.
 ---
@@ -98,7 +98,7 @@ We encourage you to migrate your code for both CLI and SDK v1 to CLI and SDK v2.
 
 ### CLI v2
 
-Azure Machine Learning CLI v1 has been deprecated. Support for the v1 extension will end on September 30, 2025. You will be able to install and use the v1 extension until that date.
+Azure Machine Learning CLI v1 has been deprecated. Support for the v1 extension will end on September 30, 2025. You'll be able to install and use the v1 extension until that date.
 
 We recommend that you transition to the `ml`, or v2, extension before September 30, 2025.
 
@@ -110,7 +110,7 @@ Azure Machine Learning Python SDK v1 doesn't have a planned deprecation date. If
 * You're starting a new workflow or pipeline. All new features and future investments will be introduced in v2.
 * You want to take advantage of the improved usability of the Python SDK v2 ability to compose jobs and pipelines by using Python functions, with easy evolution from simple to complex tasks.
 
-## Next steps
+## Related content
 
 * [Upgrade from v1 to v2](how-to-migrate-from-v1.md)
 * Get started with CLI v2: