Reference library

ML engineering pipelines

Feature prep, batch inference, model-serving hooks, and production ML workflow glue.

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ML engineering pipelines easy

How to Build a Mock Offline Feature Store in Python

Build an in-memory mock of an offline feature store with a dict-based FeatureStore class for storing and retrieving ML features by entity ID.

feature-store ml-pipeline mock
Python 
from datetime import datetime
from collections import defaultdict


class FeatureStore:
    """Simple in-memory mock of an offline feature store."""

    def __init__(self):
        self._features = defaultdict(dict)

    def ingest(self, entity_id, feature_name, value, timestamp=None):
        ts = timestamp or datet…
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