Configure MLflow workflows

[SaashaJoshi]

Configure MLflow workflows for training, model and parameter logging, and model registry for deployment purposes.

Things to add:

• [x] MLproject file that specifies an entry point of the mlflow script.
• [x] conda_env file
• [ ] Other script files that are called from MLproject
• [x] PyFunc wrapper for QiskitModel
• [ ] mlflow workflow config file

Also, refer to this multi-step workflow example here.

[SaashaJoshi]

The QML model, in the form of a NeuralNetworkClassifier, does not conform to the mlflow.pyfunc.PythonModel instance and cannot be saved through mlflow.pyfunc.save_model class method.

One possible solution is to build a wrapper around the EstimatorQNN primitive and the NeuralNetworkClassifier so that it represents a PythonModel that evaluates inputs and produces API-compatible outputs when called after training.

Refer Issue #58

[SaashaJoshi]

Models perhaps need to be made Serializable to support saving and loading. Refer here.

[SaashaJoshi]

Also, add configuration for registering the trained model in a Model Registry. For example,

- name: Log with MLflow and Register Model
        run: |
          mlflow run . --experiment-name=${{ secrets.MLFLOW_EXPERIMENT_NAME }} --no-conda
          mlflow models register -m "runs:/${{ github.sha }}/model" -n "your_model_name" -r "your_model_version" --experiment-name="${{ secrets.MLFLOW_EXPERIMENT_NAME }}" --no-conda
        env:
          MLFLOW_TRACKING_URI: ${{ secrets.MLFLOW_TRACKING_URI }}  
          MLFLOW_REGISTRY_URI: ${{ secrets.MLFLOW_REGISTRY_URI }}  
          MLFLOW_EXPERIMENT_NAME: ${{ secrets.MLFLOW_EXPERIMENT_NAME }} 

[SaashaJoshi]

This PR is transferred to another GitHub repo. Refer here.