MLOps will help you to understand how to build the Continuous Integration and Continuous Delivery pipeline for a ML/AI project. We will be using the Azure DevOps Project for build and release/deployment pipelines along with Azure ML services for model retraining pipeline, model management and operationalization.
This template contains code and pipeline definition for a machine learning project demonstrating how to automate an end to end ML/AI workflow. The build pipelines include DevOps tasks for data sanity test, unit test, model training on different compute targets, model version management, model evaluation/model selection, model deployment as realtime web service, staged deployment to QA/prod and integration testing.
This reference architecture shows how to implement continuous integration (CI), continuous delivery (CD), and retraining pipeline for an AI application using Azure DevOps and Azure Machine Learning. The solution is built on the scikit-learn diabetes dataset but can be easily adapted for any AI scenario and other popular build systems such as Jenkins and Travis.
2. Once the Azure DevOps build pipeline is triggered, it performs code quality checks, data sanity tests, unit tests, builds an [Azure ML Pipeline](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-ml-pipelines) and publishes it in an [Azure ML Service Workspace](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace).
3. The [Azure ML Pipeline](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-ml-pipelines) is triggered once the Azure DevOps build pipeline completes. All the tasks in this pipeline runs on Azure ML Compute. Following are the tasks in this pipeline:
- **Train Model** task executes model training script on Azure ML Compute. It outputs a [model](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-azure-machine-learning-architecture#models) file which is stored in the [run history](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-azure-machine-learning-architecture#runs).
- **Evaluate Model** task evaluates the performance of the newly trained model with the model in production. If the new model performs better than the production model, the following steps are executed. If not, they will be skipped.
- **Register Model** task takes the improved model and registers it with the [Azure ML Model registry](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-azure-machine-learning-architecture#model-registry). This allows us to version control it.
The [Azure DevOps multi-stage pipeline](https://docs.microsoft.com/en-us/azure/devops/pipelines/process/stages?view=azure-devops&tabs=yaml) packages the new model along with the scoring file and its python dependencies into a [docker image](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-azure-machine-learning-architecture#image) and pushes it to [Azure Container Registry](https://docs.microsoft.com/en-us/azure/container-registry/container-registry-intro). This image is used to deploy the model as [web service](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-azure-machine-learning-architecture#web-service) across QA and Prod environments. The QA environment is running on top of [Azure Container Instances (ACI)](https://azure.microsoft.com/en-us/services/container-instances/) and the Prod environment is built with [Azure Kubernetes Service (AKS)](https://docs.microsoft.com/en-us/azure/aks/intro-kubernetes).
