added ML-For-Beginners and contribution_plan.md and contribut…

.ci/python-keras-scoring.yml

@@ -103,4 +103,4 @@ jobs:
       fieldMappings: |
         Description=Branch: Branch $(Build.SourceBranch) failed to build. Go to Boards>WorkItems and tag the failure type.
     displayName: 'Create work item on failure'
-    condition: failed()    
+    condition: failed()

.ci/python-ml-scoring.yml

@@ -68,4 +68,4 @@ jobs:
       fieldMappings: |
         Description=Branch: Branch $(Build.SourceBranch) failed to build. Go to Boards>WorkItems and tag the failure type.
     displayName: 'Create work item on failure'
-    condition: failed()    
+    condition: failed()

.ci/python-ml-training.yml

@@ -115,4 +115,4 @@ jobs:
       fieldMappings: |
         Description=Branch: Branch $(Build.SourceBranch) failed to build. Go to Boards>WorkItems and tag the failure type.
     displayName: 'Create work item on failure'
-    condition: failed()
+    condition: failed()

.ci/realtime-serving-dl-dev.yml

@@ -1,4 +1,3 @@
-
 # Starter pipeline
 # Start with a minimal pipeline that you can customize to build and deploy your code.
 # Add steps that build, run tests, deploy, and more:

.ci/scripts/SetResource.ps1

@@ -79,4 +79,4 @@ $clusterResources = Get-AzResource -ResourceType "Microsoft.ContainerService/man
 foreach($cluster in $clusterResources)
 {
     Update-GroupResources -resGroup $cluster.Properties.nodeResourceGroup -tags $projectTags
-}
+}

.ci/steps/DLAKSDeployAMLJob.yml

@@ -1,5 +1,3 @@
-
-
 parameters:
   azureSubscription: ''
   azure_subscription: ''

.ci/steps/MLAKSDeployAMLJob.yml

@@ -1,5 +1,3 @@
-
-
 parameters:
   azureSubscription: ''
   azure_subscription: ''
@@ -93,4 +91,3 @@ steps:
     conda: ${{parameters.conda}}
     azureresourcegroup: ${{parameters.azureresourcegroup}}
     doCleanup: ${{parameters.doCleanup}}
-

.ci/steps/MLBatchDeployAMLJob.yml

@@ -1,4 +1,3 @@
-
 parameters:
   azureSubscription: ''
   azure_subscription: ''

.ci/steps/RecoPySparkRTS.yml

@@ -1,4 +1,3 @@
-
 parameters:
   azureSubscription: ''
   azure_subscription: ''

.ci/steps/az-ml-realtime-score.yml

@@ -1,5 +1,3 @@
-
-
 parameters:
   azureSubscription: ''
   azure_subscription: ''

.ci/steps/azpapermill.yml

@@ -1,5 +1,3 @@
-
-
 parameters:
   notebook: 01_DataPrep.ipynb  # defaults for any parameters that aren't specified
   location: "x"

.ci/steps/azpapermill_iterator.yml

@@ -1,5 +1,3 @@
-
-
 parameters:
   notebooks: 01_DataPrep.ipynb  # defaults for any parameters that aren't specified
   location: "x"

.ci/steps/azure_r.yml

@@ -1,4 +1,3 @@
-
 parameters:
   notebook: # defaults for any parameters that aren't specified
   location: "."

.ci/steps/bash_r.yml

@@ -1,4 +1,3 @@
-
 parameters:
   notebook: # defaults for any parameters that aren't specified
   location: "."

.ci/steps/cleanuptask.yml

@@ -22,4 +22,3 @@ steps:
         echo Project resource group did not exist
       fi
       echo Done Cleanup
-

.ci/steps/config_conda.yml

@@ -1,4 +1,3 @@
-
 parameters:
   conda_location: .
   azureSubscription: #
@@ -105,4 +104,3 @@ steps:
 
       pip install -U "azureml-core<0.1.5" "azureml-contrib-services<0.1.5" "azureml-pipeline<0.1.5" \
         --extra-index-url https://azuremlsdktestpypi.azureedge.net/sdk-release/master/588E708E0DF342C4A80BD954289657CF
-

.ci/steps/deploy_rts.yml

@@ -108,4 +108,3 @@ steps:
       location: ${{parameters.location}}
       azureSubscription: ${{parameters.azureSubscription}}
       conda: ${{parameters.conda}}
-

.ci/steps/deploy_steps.yml

@@ -73,4 +73,3 @@ steps:
     ScriptArguments: '-resourceGroupName ''${{parameters.azureresourcegroup}}'' -tagId ''deployment-id'' -deploymentId ''${{parameters.deploymentguidtag}}'''
     azurePowerShellVersion: 'LatestVersion'
   displayName: 'Tag All Resources'
-

.ci/steps/deploy_steps_v2.yml

@@ -85,4 +85,3 @@ steps:
     ScriptArguments: '-resourceGroupName ''${{parameters.azureresourcegroup}}'' -tagId ''deployment-id'' -deploymentId ''${{parameters.deploymentguidtag}}'''
     azurePowerShellVersion: 'LatestVersion'
   displayName: 'Tag All Resources'
-

.ci/steps/docker_clean_step.yml

@@ -1,4 +1,3 @@
-
 steps:
 - script: |
     docker stop $(docker ps -a -q)

.ci/steps/papermill.yml

@@ -1,5 +1,3 @@
-
-
 parameters:
   notebook: 01_DataPrep.ipynb  # defaults for any parameters that aren't specified
   location: "{{cookiecutter.project_name}}"

.ci/steps/reco_conda_clean_win.yml

@@ -17,4 +17,4 @@ steps:
     del /q /S %LOCALAPPDATA%\Temp\*
     for /d %%i in (%LOCALAPPDATA%\Temp\*) do @rmdir /s /q "%%i"
   displayName: 'Remove Temp Files'
-  condition: succeededOrFailed()  
+  condition: succeededOrFailed()

.ci/steps/shorten_string.yml

@@ -1,4 +1,3 @@
-
 parameters:
   Input_String: ""
   Output_Variable: ""

.docs/python_scoring.md

@@ -4,4 +4,4 @@
   <img width="800" src="../images/decision_python_scoring.png">
 </p>
 
-When deploying ML models in Python there are two core questions. The first is will it be real time and whether the model is a deep learning model. For deploying deep learning models that require real time we recommend Azure Kubernetes Services (AKS) with GPUs. For a tutorial on how to do that look at [AKS w/GPU](https://github.com/Microsoft/AKSDeploymentTutorialAML). For deploying deep learning models for batch scoring we recommend using AzureML pipelines with GPUs, for a tutorial on how to do that look [AzureML Pipelines w/GPU](https://github.com/Azure/Batch-Scoring-Deep-Learning-Models-With-AML). For non deep learning models we recommend you use the same services but without GPUs. For a tutorial on deploying classical ML models for real time scoring look [AKS](https://github.com/Microsoft/MLAKSDeployAML) and for batch scoring [AzureML Pipelines](https://github.com/Microsoft/AMLBatchScoringPipeline)
+When deploying ML models in Python there are two core questions. The first is will it be real time and whether the model is a deep learning model. For deploying deep learning models that require real time we recommend Azure Kubernetes Services (AKS) with GPUs. For a tutorial on how to do that look at [AKS w/GPU](https://github.com/Microsoft/AKSDeploymentTutorialAML). For deploying deep learning models for batch scoring we recommend using AzureML pipelines with GPUs, for a tutorial on how to do that look [AzureML Pipelines w/GPU](https://github.com/Azure/Batch-Scoring-Deep-Learning-Models-With-AML). For non deep learning models we recommend you use the same services but without GPUs. For a tutorial on deploying classical ML models for real time scoring look [AKS](https://github.com/Microsoft/MLAKSDeployAML) and for batch scoring [AzureML Pipelines](https://github.com/Microsoft/AMLBatchScoringPipeline)

.docs/python_training.md

@@ -7,4 +7,4 @@
 There are many options for training ML models in Python on Azure. The most straight forward way is to train your model on a [DSVM](https://azure.microsoft.com/en-us/services/virtual-machines/data-science-virtual-machines/). You can either do this in local model straight on the VM or through attaching it in AzureML as a compute target. If you want to have AzureML manage the compute for you and scale it up and down based on whether jobs are waiting in the queue then you should AzureML Compute. 
 
 Now if you are going to run multiple jobs for hyperparameter tuning or other purposes then we would recommend using [Hyperdrive](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-tune-hyperparameters), [Azure automated ML](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-automated-ml) or AzureML Compute dependent on your requirements.
-For a tutorial on how to use Hyperdrive go [here](https://github.com/Microsoft/MLHyperparameterTuning).
+For a tutorial on how to use Hyperdrive go [here](https://github.com/Microsoft/MLHyperparameterTuning).

.github/ISSUE_TEMPLATE/scenario_request.md

@@ -13,4 +13,4 @@ assignees: ''
 ### Reasons for scenario
 <!---  Outline core reasons why the scenario should be covered -->
 
-### Other Comments
+### Other Comments

LICENSE

@@ -1,4 +1,4 @@
-    MIT License
+MIT License
 
     Copyright (c) Microsoft Corporation. All rights reserved.
 
@@ -18,4 +18,4 @@
     AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
     LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
     OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-    SOFTWARE
+    SOFTWARE