Merge pull request #78338 from jackharter5/master

Added Note to concept-azure-machine-learning-architecture.md about .a…

Author: PRMerger19

articles/machine-learning/service/concept-accelerate-with-fpgas.md

@@ -75,7 +75,7 @@ To get started deploying trained DNN models to FPGAs in the Azure cloud, see [De
 
 Check out these videos and blogs:
 
-+ [Hyperscale hardware: ML at scale on top of Azure + FPGA : Build 2018 (video)](https://www.youtube.com/watch?v=BMgQAHIx2eY)
++ [Hyperscale hardware: ML at scale on top of Azure + FPGA : Build 2018 (video)](https://channel9.msdn.com/events/Build/2018/BRK3202)
 
 + [Inside the Microsoft FPGA-based configurable cloud (video)](https://channel9.msdn.com/Events/Build/2017/B8063)
 

articles/machine-learning/service/concept-azure-machine-learning-architecture.md

@@ -160,6 +160,9 @@ When you start a training run where the source directory is a local Git reposito
 
 When you submit a run, Azure Machine Learning compresses the directory that contains the script as a zip file and sends it to the compute target. The zip file is then extracted, and the script is run there. Azure Machine Learning also stores the zip file as a snapshot as part of the run record. Anyone with access to the workspace can browse a run record and download the snapshot.
 
+> [!NOTE]
+> To prevent unnecessary files from being included in the snapshot, make an ignore file (.gitignore or .amlignore). Place this file in the Snapshot directory and add the filenames to ignore in it. The .amlignore file uses the same [syntax and patterns as the .gitignore file](https://git-scm.com/docs/gitignore). If both files exist, the .amlignore file takes precedence.
+
 ## Activity
 
 An activity represents a long running operation. The following operations are examples of activities: