Overview
========

Training a great model can take a lot of work. Getting that model into production should be quick and easy. ADS has a set of classes that take your model and push it to production with a few quick steps.

The first step is to create a model serialization object. This object wraps your model and has a number of methods to assist in deploying it. There are different model classes for different model classes. For example, if you have a PyTorch model you would use the ``PyTorchModel`` class. If you have a TensorFlow model you would use the ``TensorFlowModel`` class. ADS has model serialization for many different model classes. However, it is not feasible to have a model serialization class for all model types. Therefore, the ``GenericModel`` can be used for any class that has a ``.predict()`` method.

After creating the model serialization object, the next step is to use the ``.prepare()`` method to create the model artifacts. The ``score.py`` file is created and it is customized to your model class. You may still need to modify it for your specific use case but this is generally not required. The ``.prepare()`` method also can be used to store metadata about the model, code used to create the model, input and output schema, and much more.

If you make changes to the ``score.py`` file, call the ``.verify()`` method to confirm that the ``load_model()`` and ``predict()`` functions in this file are working. This speeds up your debugging as you do not need to deploy a model to test it.

The ``.save()`` method is then used to store the model in the model catalog. A call to the ``.deploy()`` method creates a load balancer and the instances needed to have an HTTPS access point to perform inference on the model. Using the ``.predict()`` method, you can send data to the model deployment endpoint and it will return the predictions.

.. figure:: figures/flow.png
   :align: center