Merge pull request #81 from Azure-Samples/archive

Archive AML

Author: HeidiSteen

README.md

@@ -4,8 +4,11 @@ This repository contains Python code samples used in Azure Cognitive Search docu
 
 | Sample | Description |
 |--------|-------------|
-| azureml-custom-skill | This sample is a Jupyter Python3 .ipynb file. It's used in the [Tutorial: Build and deploy a custom skill with Azure Machine Learning](https://docs.microsoft.com/azure/search/cognitive-search-tutorial-aml-custom-skill). This sample provides an end-to-end walk through for training and deploying an aspect-based sentiment model to an Azure Kubernetes cluster for consumption as a custom skill in a Cognitive Search enrichment pipeline. Azure Machine Learning is used to train and deploy the model. |
 | image-processing | This sample is a Jupyter Python3 .ipynb file that shows how to work with image skills in a skillset. Although the skillset performs useful operations, including Optical Character Recognition (OCR) and redaction of personally identifying information, the sample's purpose is to demonstrate the coordination of image file handoffs from one skill to the next. |
 | quickstart | "Day One" introduction to the fundamental tasks of working with a search index: create, load, and query. This sample is a Jupyter Python3 .ipynb file. The index is modeled on a subset of the Hotels dataset, widely used in Cognitive Search samples, but reduced here for readability and comprehension. |
 | tutorial-ai-enrichment | This sample is a Jupyter Python3 .ipynb file used in the [Python Tutorial: Call Cognitive Services APIs in an Azure Cognitive Search indexing pipeline](https://docs.microsoft.com/azure/search/cognitive-search-tutorial-blob-python). This sample demonstrates cognitive search functionality, adding AI enrichments from Cognitive Services to extract, detect, and analyze information from image files or large unstructured document files. |
 | search-website-functions-v4 | Shows how to create, load, and query a search index in Python using the Azure.Search.Documents library in the Azure SDK for Python. It also includes application code and sample data so that you can see search integration in the context of a full app. The data is from [https://github.com/zygmuntz/goodbooks-10k](https://github.com/zygmuntz/goodbooks-10k). The app is an Azure Static Web app, using the React library for user interaction, and Azure Function to handle the query requests and responses in the application layer. |
+
+| Archived sample | Description |
+|--------|-------------|
+| (archived) azureml-custom-skill | This archived sample is out of support. |

AzureML-Custom-Skill/CONTRIBUTING.md …ive/azureml-custom-skill/CONTRIBUTING.mdAzureML-Custom-Skill/CONTRIBUTING.md renamed to archive/azureml-custom-skill/CONTRIBUTING.md AzureML-Custom-Skill/CONTRIBUTING.md renamed to archive/azureml-custom-skill/CONTRIBUTING.md

@@ -1,21 +1,11 @@
----
-topic: sample
-languages: 
- - python
- - REST
-name: Build and deploy a custom skill with Azure Machine Learning
-description: This sample provides a Jupyter notebook for building and training an aspect-based sentiment model with Azure Machine Learning and then deploying the model to an Azure Kubernetes cluster for integration into an AI enrichment pipeline in Azure Cognitive Search.
-products: 
- - azure
- - azure-cognitive-search
- - azure-machine-learning
-urlFragment: custom-skill-azure-machine-learning
----
-
-# Build and deploy a custom skill with Azure Machine Learning and Azure Cognitive Search
+# Archived - Custom skill example using Azure Machine Learning and Azure Cognitive Search
 
 ![MIT license badge](https://img.shields.io/badge/license-MIT-green.svg)
 
+This example is archived and no longer supported. The text of this readme is for the original sample and is out of date.
+
+## Original introduction
+
 In this sample, you will use the [hotel reviews dataset](https://www.kaggle.com/datafiniti/hotel-reviews) (distributed under [the Creative Commons license CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)) to create a custom skill using Azure Machine Learning to extract aspect-based sentiment from the reviews. This allows for the assignment of positive and negative sentiment within the same review to be correctly ascribed to identified entities like staff, room, lobby, or pool.
 
 To train the aspect-based sentiment model, you will be using the [nlp recipes repository](https://github.com/microsoft/nlp-recipes/tree/master/examples/sentiment_analysis/absa). The model will then be deployed as an endpoint on an Azure Kubernetes cluster. Once deployed, the model is added to the enrichment pipeline as a custom skill for use by the Cognitive Search service.