Online vs offline feature stores in MLOps - CLI Comparison

Introduction

Feature stores help manage data features used in machine learning. Online feature stores provide fast access to fresh data for real-time predictions. Offline feature stores store historical data for training and batch processing.

When you need to serve real-time predictions with the latest data in a web app or mobile app

When you want to train machine learning models using historical data stored in a data warehouse

When you want to keep feature data consistent between training and serving environments

When you want to reduce data engineering work by centralizing feature management

When you want to monitor feature data quality and freshness over time

Commands

This command creates an offline feature store using Parquet files stored at /data/offline_features. Offline stores hold historical feature data for training.

Terminal

mlflow feature-store create-offline-store --name example_offline_store --type parquet --path /data/offline_features

Expected Output

Created offline feature store 'example_offline_store' with type 'parquet' at path '/data/offline_features'

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--name - Sets the name of the offline feature store

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--type - Specifies the storage type for offline features

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--path - Defines the storage location for offline features

This command creates an online feature store using Redis for fast, low-latency access to features during model serving.

Terminal

mlflow feature-store create-online-store --name example_online_store --type redis --host 127.0.0.1 --port 6379

Expected Output

Created online feature store 'example_online_store' with type 'redis' at 127.0.0.1:6379

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--name - Sets the name of the online feature store

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--type - Specifies the storage type for online features

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--host - Defines the Redis server host

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--port - Defines the Redis server port

This command ingests historical user feature data into the offline feature store from a Parquet file for training use.

Terminal

mlflow feature-store ingest --store example_offline_store --feature-group user_features --file user_features.parquet

Expected Output

Ingested 10000 records into feature group 'user_features' in offline store 'example_offline_store'

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--store - Specifies which feature store to ingest data into

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--feature-group - Defines the feature group name

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--file - Specifies the data file to ingest

This command starts serving the user_features from the online feature store for real-time model predictions.

Terminal

mlflow feature-store serve --store example_online_store --feature-group user_features

Expected Output

Serving feature group 'user_features' from online store 'example_online_store' on port 8080

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--store - Specifies which online feature store to serve from

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--feature-group - Defines the feature group to serve

Key Concept

If you remember nothing else from this pattern, remember: offline feature stores hold historical data for training, while online feature stores provide fast access to fresh data for real-time predictions.

Common Mistakes

Using the online feature store for batch training data ingestion

Online stores are optimized for low-latency access, not large-scale batch storage, which can cause performance issues

Use the offline feature store to ingest and store historical data for training

Not keeping feature definitions consistent between online and offline stores

This causes training-serving skew, where models see different feature values during training and prediction

Define features once and use the same definitions in both stores

Serving features from the offline store in real-time applications

Offline stores have higher latency and may have stale data, causing slow or inaccurate predictions

Serve features from the online store for real-time prediction needs

Summary

Create offline feature stores to hold historical data for training machine learning models.

Create online feature stores to serve fresh features quickly for real-time predictions.

Ingest data into offline stores for batch processing and serve features from online stores during model serving.