This is a tutorial repo of Course 5 - Machine Learning in Production - MLOps organized by DataScienceWorld.Kan. Its' oriented target towards building an Azure DevOps CI/CD pipeline that comprises the steps as follows:

1. Setup infrastructure over Azure ML workspace
2. Connect to Azure ML workspace
3. Running register Dataset and Datastore
4. Training a machine learning model
5. Endpoint deployment of machine learning model
6. Test model performance.

Source: Azure Machine Learning Architecture

To manipulate datasets and train models you need to initialize an AzureML working space as a priority. The Lession 9 - Part II - Deploying infrastructure as code has provided a hands-on guideline for your to complete this.

1. Get default workspace:

python aml-service/00-Workspace.py -config config/dev/config.json

2. Load datastore:

python aml-service/10-Datastore.py -config config/dev/config.json

3. Register Dataset:

python aml-service/22-TabularDataset.py -config config/dev/config.json

4. Initializing compute instances:

python aml-service/30-Compute.py -config config/dev/config.json

5. Register Environments:

python aml-service/40-Environment.py -config config/dev/config.json

6. Model training pipeline:

python aml-service/50-PipelineModelTraining.py -config config/dev/config.json -pipeline modeltraining

7. Model deployment Local service:

python aml-service/77-DeployToLocalService.py -config config/dev/config.json

8. Testing local deployment:

python aml-service/78-TestLocal.py -config config/dev/config.json