The Feature Store is the essential part of AI infrastructure that helps organisations bring modern enterprise data to analytical and operational ML systems. It is the simplest most powerful way to get your models to production. From anywhere, to anywhere. You will load starting data into the feature store, create two feature groups from which we will make a feature view and training dataset, and train a model to predict fare amounts. Also, you will design a data-generating and Feature Store insertion pipeline, that will be running once a time using GitHub actions.
Streamlit app will be created so you would be able to try your model interactively.
This is an online use case, you will use Hopsworks Model Registry to register the model and run online deployment, which will make prediction for you.
- Backfill Features to the Feature Store,
- Create a feature pipeline,
- Create Feature views & Training Datasets,
- Train a model and upload it to the Model Registry and run deployment,
- Fetch model from Model Registry and make batch predictions,
- Deploy Streamlit app.
To run this tutorial, you need an account on Hopsworks. You can create a new account at app.hopsworks.ai. In the notebook you will be prompted with a link to generate an API token to interact with your Hopsworks account.
Also, you obviously need to have streamlit python library installed.
You will parse timeseries Bitcoin data from Binance using your own credentials, so you have to get a free Binance account and create API-keys.
Also, you should contact Twitter for their API-keys.
Don't forget to create an .env configuration file where all the necessary environment variables will be stored:
To run streamlit app (after you have run all notebooks and already have required feature groups in Feature Store and model in Model Registry), simply type:
python -m streamlit run streamlit_app.py on Windows
or
python3 -m streamlit run streamlit_app.py on Unix
