From 3f3b2d1b24e3834043704895c9404a95fc4e3bbf Mon Sep 17 00:00:00 2001 From: Jen Looper Date: Sat, 11 Sep 2021 14:38:23 -0400 Subject: [PATCH] tiny edit for consistency --- 5-Data-Science-In-Cloud/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/5-Data-Science-In-Cloud/README.md b/5-Data-Science-In-Cloud/README.md index f5813623..507c8431 100644 --- a/5-Data-Science-In-Cloud/README.md +++ b/5-Data-Science-In-Cloud/README.md @@ -2,7 +2,7 @@ ![cloud-picture](img/cloud-picture.jpg) -Photo by [Jelleke Vanooteghem](https://unsplash.com/@ilumire) from [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) +> Photo by [Jelleke Vanooteghem](https://unsplash.com/@ilumire) from [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) When it comes to doing data science with big data, the cloud can be a game changer. In the next three lessons, we are going to see what the cloud is and why it can be very helpful. We are also going to explore a heart failure dataset and build a model to help assess the probability of someone having a heart failure. We will use the power of the cloud to train, deploy and consume a model in two different ways. One way using only the user interface in a Low code/No code fashion, the other way using the Azure Machine Learning Software Developer Kit (Azure ML SDK).