Module · MLOps Engineer
Feature Stores
The same features, computed once, served to training and to production alike.
TOPIC 01
Defining Features
Entities, features and the transformations that produce them - written down once.
What you'll practice
- Define a Feature View — guided · beginner
- Write the Feature Transformation — practice · intermediate
A command you'll meet
cat ~/feast/feature_repo.yamlA feature nobody can explain is a feature nobody can trust. Open the task below for the full walkthrough.
Hands-On Tasks in This Topic (2)
Define a Feature View
A feature view names the entity, the features and where the data comes from. Write the definition for a user activity feature set.
Write the Feature Transformation
The transformation is where the raw events become the columns a model trains on. Write it once, with a schema, so training and serving agree.
TOPIC 02
Online & Offline Parity
Serving fresh values online, materialising them offline, and keeping the two in step.
What you'll practice
- Configure the Online Store — practice · intermediate
- Materialise Features Incrementally — challenge · advanced
A command you'll meet
cat ~/feast/online_store.yamlTraining skew starts where the offline and online stores disagree. Open the task below for the full walkthrough.
Hands-On Tasks in This Topic (2)
Configure the Online Store
The online store is the low-latency copy your model reads at request time. Write its configuration - where it lives, how it is keyed and how long values stay fresh.
Materialise Features Incrementally
Fresh values have to travel from the offline store into the online one. Write the job that does it, with the window it covers.