From a4fef0daeee952d9dcbac4cd31c4d8eb8454f9e5 Mon Sep 17 00:00:00 2001 From: Akshaya Annavajhala Date: Mon, 10 Aug 2020 16:17:36 -0700 Subject: [PATCH] Update README.md --- how-to-use-azureml/track-and-monitor-experiments/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/how-to-use-azureml/track-and-monitor-experiments/README.md b/how-to-use-azureml/track-and-monitor-experiments/README.md index 12cd8403..0401438c 100644 --- a/how-to-use-azureml/track-and-monitor-experiments/README.md +++ b/how-to-use-azureml/track-and-monitor-experiments/README.md @@ -10,8 +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. [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)