jrderuiter/code-breakfast-productionizing

Hackathon: productionizing predictive models

The goal of this hackathon is to illustrate the process of converting a predictive machine learning model from a proof-of-concept notebook into a Python package that can (more easily) be deployed into production. As the hackathon does not focus on developing the actual model, we will use an adapted model from the Kaggle titanic challenge https://www.kaggle.com/c/titanic, in which predictive models are trained to predict the chances of a passengers survival based on their details.

In summary, the overall goals of this Hackathon are to:

• Determine the basic building blocks of the titanic model
• Implement these blocks in a Python package (including tests + documentation)
• Wrap the model in a REST API using Flask
• Containerize the application using Docker (optional)

Step 1: Run the existing notebook

First, to see what the existing model does, we will start by running the existing notebook.

• Create a clean environment using conda or virtualenv and install the notebooks dependencies into that environment (using the supplied environment.yml file or the requirements.txt file).
• Run the notebook and check it's outputs.
• Can you determine the key steps involved in training the model?
  • Which feature engineering steps are involved?
  • What model is used? With which parameters?

Step 2: Create a titanic package using the provided skeleton.

In the skeleton folder, we have provided an initial setup for a titanic package that we can use for productionizing the model. We will work on expanding this skeleton into the fully fledged solution by implementing the model in a scikit-learn pipeline that can be persisted and loaded to/from disk.

• Install the package + its development dependencies into your virtualenv using pip install .[dev] (run from the titanic package directory) so that you can start developing the package.
• An initial setup for the model is provided in titanic/model.py by the TitanicModel class. Expand this class by adding implementations for the fit/predict methods.
• Optional - Run pylint over your code using make lint to see how well your code fits the pylint style. Note that you can modify the rules used by pylint by creating a pylintrc configuration file (using pylint --generate-rcfile > pylintrc).
• Optional - Try running the supplied unit tests using make tests to test if your implementation passes the supplied (very basic) unit tests.
• Optional - Add documentation (docstrings) to the TitanicModel class. You can generate HTML documentation with Sphinx using the make docs command.

Step 3: Try fitting a model using the provided CLI commands.

We have provided several command-line commands for fitting your model and saving it to a serialized file, which we can load (and later expose in an API) for producing predictions.

• Train a model on our train dataset using titanic fit data/train.csv. This should create a file called model.pkl containing your serialized model.
• Try producing predictions using this serialized model using the command titanic predict model.pkl data/test.csv. This should produce a file called predictions.csv containing your predictions.

Step 4: wrap the model in a API using Flask

Next, we will wrap our model in a Flask app so that we can serve predictions over a web API.

• Try starting the skeleton Flask application using the titanic serve model.pkl command. You can test the application by opening http://localhost:5000/ping in your browser. This should display the text pong if everything is working correctly.
• Extend the basic (class-based) Flask app implementation in titanic/app.py to implement a predict endpoint that takes CSV data from a request and passes it to your model. Note that this requires you to load your serialized model and expose it in the Scorer class.
• Try sending a dataset to the predict endpoint (using a POST request) to see if your implementation works. This request should include your prediction dataset as data in the request body. (Tip: useful tools for sending requests are the Postman application or the Python library requests. Ask for help if you need more details.)
• Optional: implement a fit endpoint that allows re-training of the model using a data set passed via a request.

Step 5: Wrap the package in a docker container

Finally, to make running/deploying our package easier, we want to create a docker image containing our titanic package.

• Create a Dockerfile that installs the package + it's required dependencies and serves a model when the container is started.
• Think about how we can serve different models using the same image.