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https://github.com/csiebler/mlops-demo

Demo for MLOps with Azure Machine Learning
https://github.com/csiebler/mlops-demo

azure-devops azure-machine-learning

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Demo for MLOps with Azure Machine Learning

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README 

        
# mlops-demo



This repo shows some introduction examples to Azure Machine Learning and a simple MLOps implemenation for automating model training and deployment.



## Setup & Demo Flow



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



### Interactive demo part



1. If required, create an Azure Machine Learning workspace

1. Create a tabular dataset from [`data/german_credit_data.csv`](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)

1. Create a file dataset from [`data/german_credit_data.csv`](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)

1. Clone the whole repo into a Compute Instance

1. Walk through the following notebooks

    * [`models/german-credit-basic/notebooks/german-credit-local.ipynb`](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`](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`](models/german-credit-basic/notebooks/deploy_webservices.ipynb) - Shows how to deploy the trained model to an Azure Container Instance

1. For deployment to AKS, make sure to create an AKS cluster in AML that has `SSL enabled` (using the Microsoft certificate)



### MLOps demo part



#### Simple MLOps pipelines



1. Create a new project in Azure DevOps

1. Fork this repo or import it into Azure DevOps (so that you can make changes to the repo)

1. Create a service connection to your Azure Machine Learning workspace and use the name `aml-workspace-connection`

1. Edit [`pipelines/german-credit-config.yml`](pipelines/german-credit-config.yml) and adapt the values to point to your workspace

1. Import the following pipelines into DevOps

    * [`pipelines/german-credit-train-and-register.yml`](pipelines/german-credit-train-and-register.yml) - Trains and registers the model automatically

    * [`pipelines/german-credit-deploy.yml`](pipelines/german-credit-deploy.yml) - Deploys the trained model to AKS

1. Run the pipelines



#### Staged MLOps pipelines



1. First, get the simple pipelines from [`pipelines/`](pipelines/) running

1. Edit [`pipelines-staged/german-credit-config-dev.yml`](pipelines-staged/german-credit-config-dev.yml) and  [`pipelines-staged/german-credit-config-prod.yml`](pipelines-staged/german-credit-config-prod.yml) and adapt the values to point to your dev and prod workspaces

1. Import the following pipeline into DevOps

    * [`pipelines-staged/german-credit-rollout.yml`](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

1. Run the pipeline



## Conventions



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...

```



## Testing



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



```python

import requests

import json



url = ''

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)

```



## Further Work



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