This repo shows a simple MLOps implementation for automating model training and deployment.

This gives a short, high-level overview of how this repo may be used.

1. If required, create an Azure Machine Learning workspace
2. Create a tabular dataset from data/german_credit_data.csv and name it german_credit_dataset (download the file to your machine and select From Local File when creating a new Dataset)
3. Create a file dataset from data/german_credit_data.csv and name it german_credit_file (use From Datastore and point to the same file as in the prior step)
4. Clone the whole repo into a Compute Instance
5. Walk through the following notebooks
   • models/german-credit-basic/notebooks/german-credit-local.ipynb - Shows how to run local training inside the Compute Instance, registers the model with data linage, and calculates the model explainability
   • models/german-credit-basic/notebooks/german-credit-amlcompute.ipynb - Shows how to train the same model on a Compute Cluster
   • models/german-credit-basic/notebooks/deploy_webservices.ipynb - Shows how to deploy the trained model to an Azure Container Instance
6. For deployment to AKS, make sure to create an AKS cluster in AML that has SSL enabled (using the Microsoft certificate)

1. Create a new project in Azure DevOps
2. Fork this repo or import it into Azure DevOps (so that you can make changes to the repo)
3. Create a service connection to your Azure Machine Learning workspace and use the name aml-workspace-connection
4. Edit pipelines/german-credit-config.yml and adapt the values to point to your workspace
5. Import the following pipelines into DevOps
   • pipelines/german-credit-train-and-register.yml - Trains and registers the model automatically
   • pipelines/german-credit-deploy.yml - Deploys the trained model to AKS
6. Run the pipelines

1. First, get the simple pipelines from pipelines/ running
2. Edit pipelines-staged/german-credit-config-dev.yml and pipelines-staged/german-credit-config-prod.yml and adapt the values to point to your dev and prod workspaces
3. Import the following pipeline into DevOps
   • pipelines-staged/german-credit-rollout.yml - Trains, registers, and deploys the model automatically to a Dev environment, once successful, the same is repeated in a Prod environment
4. Run the pipeline

This repo is fully based on conventions in order to make MLOps reusable and easily scaleable. The directory structure is as follows:

pipelines
    \- german-credit-config.yml - Configuration for german credit model
    \- german-credit-deploy.yml - Deployment pipeline for german credit model
    \- german-credit-train-and-register.yml - Pipline for training and registering the base german credit model
models
    \- model1
        train.py (entry file for training)
        score.py (entry file for scoring)
        \- config
            deployment-config-aks.yml - Deployment infrastructure definition (e.g., AKS configuration)
            inference-conda.yml - Conda environement definition for inferencing/scoring
            inference-config.yml - Azure Machine Learning config for inferencing
            train-conda.yml - Conda environement definition for training
    \- model2
        ...same file and folder structure...

This snipped can be used to manually showcase/test the deployed model on ACI:

import requests
import json

url = '<scoring url>'
key = '<api key>'

test_data = {
  'data': [{
    "Age": 20,
    "Sex": "male",
    "Job": 0,
    "Housing": "own",
    "Saving accounts": "little",
    "Checking account": "little",
    "Credit amount": 100,
    "Duration": 48,
    "Purpose": "radio/TV"
  }]
}

headers = {'Content-Type': 'application/json', 'Authorization': 'Bearer ' + key}
resp = requests.post(url, json=test_data, headers=headers)

print("Prediction (good, bad):", resp.text)

⭐ A fully documented starting template for Azure Machine Leraning with MLOps can be found here: microsoft/aml-acceleration-template. This includes model training, validation, testing, deployment, pipelines, and several other production-grade capabilties.