Merge branch 'master' into jenns/splitpipeline_docfix

Author: j-so

bootstrap/README.md

@@ -1,18 +1,3 @@
 # Bootstrap from MLOpsPython repository
 
-To use this existing project structure and scripts for your new ML project, you can quickly get started from the existing repository, bootstrap and create a template that works for your ML project.
-
-Bootstrapping will prepare a directory structure for your project which includes:
-
-* renaming files and folders from the base project name `diabetes_regression` to your project name
-* fixing imports and absolute path based on your project name
-* deleting and cleaning up some directories
-
-To bootstrap from the existing MLOpsPython repository:
-
-1. Ensure Python 3 is installed locally
-1. Clone this repository locally
-1. Run bootstrap.py script  
-`python bootstrap.py -d [dirpath] -n [projectname]`
-    * `[dirpath]` is the absolute path to the root of the directory where MLOpsPython is cloned
-    * `[projectname]` is the name of your ML project
+For steps on how to use the bootstrap script, please see the "Bootstrap the project" section of the [custom model guide](../docs/custom_model.md#bootstrap-the-project).

docs/custom_model.md

@@ -3,11 +3,11 @@
 This document provides steps to follow when using this repository as a template to train models and deploy the models with real-time inference in Azure ML with your own scripts and data.
 
 1. Follow the MLOpsPython [Getting Started](getting_started.md) guide
-1. Follow the MLOpsPython [bootstrap instructions](../bootstrap/README.md) to create your project starting point
+1. Bootstrap the project
 1. Configure training data
 1. [If necessary] Convert your ML experimental code into production ready code
 1. Replace the training code
-1. Update the evaluation code
+1. [Optional] Update the evaluation code
 1. Customize the build agent environment
 1. [If appropriate] Replace the score code
 
@@ -17,24 +17,36 @@ Follow the [Getting Started](getting_started.md) guide to set up the infrastruct
 
 Take a look at the [Repo Details](code_description.md) document for a description of the structure of this repository.
 
-## Follow the Bootstrap instructions
+## Bootstrap the project
 
-The [Bootstrap from MLOpsPython repository](../bootstrap/README.md) guide will help you to quickly prepare the repository for your project.
+Bootstrapping will prepare the directory structure to be used for your project name which includes:
+
+* renaming files and folders from the base project name `diabetes_regression` to your project name
+* fixing imports and absolute path based on your project name
+* deleting and cleaning up some directories
 
 **Note:** Since the bootstrap script will rename the `diabetes_regression` folder to the project name of your choice, we'll refer to your project as `[project name]` when paths are involved.
 
+To bootstrap from the existing MLOpsPython repository:
+
+1. Ensure Python 3 is installed locally
+1. From a local copy of the code, run the `bootstrap.py` script in the `bootstrap` folder
+`python bootstrap.py -d [dirpath] -n [projectname]`
+    * `[dirpath]` is the absolute path to the root of the directory where MLOpsPython is cloned
+    * `[projectname]` is the name of your ML project
+
 ## Configure training data
 
 The training ML pipeline uses a [sample diabetes dataset](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html) as training data.
 
-To use your own data:
+**Important** Convert the template to use your own Azure ML Dataset for model training via these steps:
 
 1. [Create a Dataset](https://docs.microsoft.com/azure/machine-learning/how-to-create-register-datasets) in your Azure ML workspace
 1. Update the `DATASET_NAME` and `DATASTORE_NAME` variables in `.pipelines/[project name]-variables-template.yml`
 
 ## Convert your ML experimental code into production ready code
 
-The MLOpsPython template creates an Azure Machine Learning (ML) pipeline that invokes a set of [Azure ML pipeline steps](https://docs.microsoft.com/python/api/azureml-pipeline-steps/azureml.pipeline.steps) (see `ml_service/pipelines/[project name]_build_train_pipeline.py`). If your experiment is currently in a Jupyter notebook, it will need to be refactored into scripts that can be run independantly and dropped into the template which the existing Azure ML pipeline steps utilize.
+The MLOpsPython template creates an Azure Machine Learning (ML) pipeline that invokes a set of [Azure ML pipeline steps](https://docs.microsoft.com/python/api/azureml-pipeline-steps/azureml.pipeline.steps) (see `ml_service/pipelines/[project name]_build_train_pipeline.py`). If your experiment is currently in a Jupyter notebook, it will need to be refactored into scripts that can be run independently and dropped into the template which the existing Azure ML pipeline steps utilize.
 
 1. Refactor your experiment code into scripts
 1. [Recommended] Prepare unit tests

docs/getting_started.md

@@ -1,11 +1,12 @@
 
 # Getting Started with MLOpsPython <!-- omit in toc -->
 
-This guide shows how to get MLOpsPython working with a sample ML project ***diabetes_regression***. The project creates a linear regression model to predict diabetes. You can adapt this example to use with your own project.
+This guide shows how to get MLOpsPython working with a sample ML project ***diabetes_regression***. The project creates a linear regression model to predict diabetes and has CI/CD DevOps practices enabled for model training and serving when these steps are completed in this getting started guide.
 
-We recommend working through this guide completely to ensure everything is working in your environment. After the sample is working, follow the [bootstrap instructions](../bootstrap/README.md) to convert the ***diabetes_regression*** sample into a starting point for your project.
+If you would like to bring your own model code to use this template structure, follow the [custom model](custom_model.md) guide. We recommend completing this getting started guide  with the diabetes model through ACI deployment first to ensure everything is working in your environment before converting the template to use your own model code.
 
 - [Setting up Azure DevOps](#setting-up-azure-devops)
+  - [Install the Azure Machine Learning extension](#install-the-azure-machine-learning-extension)
 - [Get the code](#get-the-code)
 - [Create a Variable Group for your Pipeline](#create-a-variable-group-for-your-pipeline)
   - [Variable Descriptions](#variable-descriptions)
@@ -33,6 +34,12 @@ You'll use Azure DevOps for running the multi-stage pipeline with build, model t
 
 If you already have an Azure DevOps organization, create a new project using the guide at [Create a project in Azure DevOps and TFS](https://docs.microsoft.com/en-us/azure/devops/organizations/projects/create-project?view=azure-devops).
 
+### Install the Azure Machine Learning extension
+
+Install the **Azure Machine Learning** extension to your Azure DevOps organization from the [Visual Studio Marketplace](https://marketplace.visualstudio.com/items?itemName=ms-air-aiagility.vss-services-azureml).
+
+This extension contains the Azure ML pipeline tasks and adds the ability to create Azure ML Workspace service connections.
+
 ## Get the code
 
 We recommend using the [repository template](https://github.com/microsoft/MLOpsPython/generate), which effectively forks the repository to your own GitHub location and squashes the history. You can use the resulting repository for this guide and for your own experimentation.
@@ -118,8 +125,6 @@ Check that the newly created resources appear in the [Azure Portal](https://port
 
 At this point, you should have an Azure ML Workspace created. Similar to the Azure Resource Manager service connection, you need to create an additional one for the Azure ML Workspace.
 
-Install the **Azure Machine Learning** extension to your Azure DevOps organization from the [Visual Studio Marketplace](https://marketplace.visualstudio.com/items?itemName=ms-air-aiagility.vss-services-azureml). The extension is required for the service connection.
-
 Create a new service connection to your Azure ML Workspace using the [Machine Learning Extension](https://marketplace.visualstudio.com/items?itemName=ms-air-aiagility.vss-services-azureml) instructions to enable executing the Azure ML training pipeline. The connection name needs to match `WORKSPACE_SVC_CONNECTION` that you set in the variable group above.
 
 ![Created resources](./images/ml-ws-svc-connection.png)