1. Clone the repository in your local machine.

2. Install Azure ML CLI v2. If you don't have it, follow the installation instructions at Install and set up the CLI (v2).

3. Create a compute named trainer-cpu or rename the compute specified in .aml/jobs/carpricer.job.yml.

4. Register the dataset:

   az ml data create -f .aml/data/product-reviews-train.yml
   az ml data create -f .aml/data/product-reviews-eval.yml

5. Create the training job:

   az ml job create -f .aml/jobs/carpricer.job.yml

(Optional)

6. Register the trained model in the registry:

   JOB_NAME=$(az ml job list --query "[0].name" | tr -d '"')
   az ml model create --name "carpricer" \
                      --type "mlflow_model" \
                      --path "azureml://jobs/$JOB_NAME/outputs/artifacts/pipeline"

7. Deploy the model in an online endpoint:

   az ml online-endpoint create -f .aml/endpoints/carpricer-online/endpoint.yml
   az ml online-deployment create -f .aml/endpoints/carpricer-online/deployments/default.yml --all-traffic

This project welcomes contributions and suggestions. Open an issue and start the discussion!