From 4beb749a768a68d98518d19a9f488606ecbdce51 Mon Sep 17 00:00:00 2001 From: Harneet Virk Date: Tue, 20 Oct 2020 12:28:45 -0700 Subject: [PATCH 1/2] Fixing/Removing the broken links --- README.md | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index c85002c1..1f5ca357 100644 --- a/README.md +++ b/README.md @@ -18,10 +18,10 @@ This [index](./index.md) should assist in navigating the Azure Machine Learning If you want to... * ...try out and explore Azure ML, start with image classification tutorials: [Part 1 (Training)](./tutorials/image-classification-mnist-data/img-classification-part1-training.ipynb) and [Part 2 (Deployment)](./tutorials/image-classification-mnist-data/img-classification-part2-deploy.ipynb). - * ...learn about experimentation and tracking run history, first [train within Notebook](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), then try [training on remote VM](./how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.ipynb) and [using logging APIs](./how-to-use-azureml/training/logging-api/logging-api.ipynb). - * ...train deep learning models at scale, first learn about [Machine Learning Compute](./how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb), and then try [distributed hyperparameter tuning](./how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb) and [distributed training](./how-to-use-azureml/training-with-deep-learning/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb). + * ...learn about experimentation and tracking run history, try [training on remote VM](./how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.ipynb). + * ...train deep learning models at scale, learn about [Machine Learning Compute](./how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb) * ...deploy models as a realtime scoring service, first learn the basics by [training within Notebook and deploying to Azure Container Instance](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), then learn how to [production deploy models on Azure Kubernetes Cluster](./how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb). - * ...deploy models as a batch scoring service, first [train a model within Notebook](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), then [create Machine Learning Compute for scoring compute](./how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb), and [use Machine Learning Pipelines to deploy your model](https://aka.ms/pl-batch-scoring). + * ...deploy models as a batch scoring service, [create Machine Learning Compute for scoring compute](./how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb), and [use Machine Learning Pipelines to deploy your model](https://aka.ms/pl-batch-scoring). * ...monitor your deployed models, learn about using [App Insights](./how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb). ## Tutorials @@ -33,13 +33,11 @@ The [Tutorials](./tutorials) folder contains notebooks for the tutorials describ The [How to use Azure ML](./how-to-use-azureml) folder contains specific examples demonstrating the features of the Azure Machine Learning SDK - [Training](./how-to-use-azureml/training) - Examples of how to build models using Azure ML's logging and execution capabilities on local and remote compute targets -- [Training with Deep Learning](./how-to-use-azureml/training-with-deep-learning) - Examples demonstrating how to build deep learning models using estimators and parameter sweeps - [Manage Azure ML Service](./how-to-use-azureml/manage-azureml-service) - Examples how to perform tasks, such as authenticate against Azure ML service in different ways. - [Automated Machine Learning](./how-to-use-azureml/automated-machine-learning) - Examples using Automated Machine Learning to automatically generate optimal machine learning pipelines and models - [Machine Learning Pipelines](./how-to-use-azureml/machine-learning-pipelines) - Examples showing how to create and use reusable pipelines for training and batch scoring - [Deployment](./how-to-use-azureml/deployment) - Examples showing how to deploy and manage machine learning models and solutions - [Azure Databricks](./how-to-use-azureml/azure-databricks) - Examples showing how to use Azure ML with Azure Databricks -- [Monitor Models](./how-to-use-azureml/monitor-models) - Examples showing how to enable model monitoring services such as DataDrift - [Reinforcement Learning](./how-to-use-azureml/reinforcement-learning) - Examples showing how to train reinforcement learning agents --- @@ -58,7 +56,7 @@ Visit this [community repository](https://github.com/microsoft/MLOps/tree/master ## Projects using Azure Machine Learning Visit following repos to see projects contributed by Azure ML users: - - [AMLSamples](https://github.com/Azure/AMLSamples) Number of end-to-end examples, including face recognition, predictive maintenance, customer churn and sentiment analysis. + - [AML Examples](https://github.com/Azure/azureml-examples) - [Learn about Natural Language Processing best practices using Azure Machine Learning service](https://github.com/microsoft/nlp) - [Pre-Train BERT models using Azure Machine Learning service](https://github.com/Microsoft/AzureML-BERT) - [Fashion MNIST with Azure ML SDK](https://github.com/amynic/azureml-sdk-fashion) From 9f364f7638e5d1e0fcd087298fcd7fd46f47421f Mon Sep 17 00:00:00 2001 From: Harneet Virk Date: Tue, 20 Oct 2020 12:30:03 -0700 Subject: [PATCH 2/2] Update README.md --- how-to-use-azureml/README.md | 1 - 1 file changed, 1 deletion(-) diff --git a/how-to-use-azureml/README.md b/how-to-use-azureml/README.md index 0accac07..3dc57c10 100644 --- a/how-to-use-azureml/README.md +++ b/how-to-use-azureml/README.md @@ -4,7 +4,6 @@ Learn how to use Azure Machine Learning services for experimentation and model m As a pre-requisite, run the [configuration Notebook](../configuration.ipynb) notebook first to set up your Azure ML Workspace. Then, run the notebooks in following recommended order. -* [train-within-notebook](./training/train-within-notebook): Train a model while tracking run history, and learn how to deploy the model as web service to Azure Container Instance. * [train-on-local](./training/train-on-local): Learn how to submit a run to local computer and use Azure ML managed run configuration. * [train-on-amlcompute](./training/train-on-amlcompute): Use a 1-n node Azure ML managed compute cluster for remote runs on Azure CPU or GPU infrastructure. * [train-on-remote-vm](./training/train-on-remote-vm): Use Data Science Virtual Machine as a target for remote runs.