TODO

• Define request data
• Define on demand transforms
  • Note that this can also transforms pushed features (e.g. stream features)
  • Note that this can combine multiple feature views and request data

In this module, we introduce the concept of on demand transforms. These are transformations that execute on-the-fly and accept as input other feature views or request data.

We and focus on building features for online serving, and keeping them fresh with a combination of batch feature materialization and stream feature ingestion. We'll be roughly working towards the following:

• Data sources: Kafka + File source
• Online store: Redis
• Use case: Predicting churn for drivers in real time.

First, we install Feast with Spark and Redis support:

pip install "feast[spark,redis]"