jprdonnelly/MachineLearningNotebooks
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Remove broken links (#1095)

Browse Source 
* Remove broken links

* Update README.md
This commit is contained in:
Akshaya Annavajhala
2020-08-10 16:35:41 -07:00
committed by GitHub
parent aac4fa1fb9
commit 79739b5e1b
1 changed files with 2 additions and 4 deletions
Download Patch File Download Diff File Expand all files Collapse all files

6
how-to-use-azureml/track-and-monitor-experiments/README.md
View File

@@ -10,10 +10,8 @@
[MLflow](https://mlflow.org/) is an open-source platform for tracking machine learning experiments and managing models. You can use MLflow logging APIs with Azure Machine Learning service: the metrics and artifacts are logged to your Azure ML Workspace.
Try out the sample notebooks:
1. [Use MLflow with Azure Machine Learning for Local Training Run](./train-local/train-local.ipynb)
1. [Use MLflow with Azure Machine Learning for Remote Training Run](./train-remote/train-remote.ipynb)
1. [Deploy Model as Azure Machine Learning Web Service using MLflow](./deploy-model/deploy-model.ipynb)
1. [Train and Deploy PyTorch Image Classifier](./train-deploy-pytorch/train-deploy-pytorch.ipynb)
1. [Use MLflow with Azure Machine Learning for Local Training Run](./using-mlflow/train-local/train-local.ipynb)
1. [Use MLflow with Azure Machine Learning for Remote Training Run](./using-mlflow/train-remote/train-remote.ipynb)
![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/track-and-monitor-experiments/README.png)