Feature Platforms: The Backbone of Real-Time ML

From Pipelines to Platforms

What are feature platforms and how they came to be

What data pipelines for ML looked like before feature platforms:

1. Lack of Standardization in Data Pipelines
   • Before feature platforms, every ML project required custom, often brittle, pipelines for sourcing, transforming, and managing training and prediction data.
   • This made reproducibility and consistency across models incredibly hard to achieve.
2. Inability to Scale Beyond Local Resources
   • Models were restricted by the memory and compute limitations of individual data scientists’ machines.
   • There was no infrastructure to train on distributed or large-scale datasets easily.
3. No Central Repository for Experiments
   • Training results were scattered across notebooks, local files, or ad hoc cloud storage.
   • Comparing experiments, tracking versions, and reproducing outcomes was nearly impossible.
4. No Reliable Path to Production
   • Even if a model performed well in development, deploying it involved heavy engineering lift.
   • Teams had to create custom containers or bespoke infrastructure every time, leading to slow iteration and operational inconsistency.
5. Fragmented Collaboration Between DS and Engineering
   • Data scientists owned feature logic, but engineers had to reimplement it for serving—introducing risk of mismatched training vs inference behavior.

Enter Feature Platforms,

1. Provide standardized, declarative pipelines for feature computation.
2. Support both batch and real-time feature generation at scale.
3. Offer versioned registries, lineage tracking, and discoverability.
4. Unify training and serving paths to ensure consistency.
5. Enable reusable infrastructure that democratizes experimentation and deployment.

Understanding “Realtime” in ML

Batch vs near real-time vs real-time. Latencies here refer to the time between an event happening and the data-point being available to an ML model as a feature for prediction

• Batch features are pre-computed in a batch process/scheduled jobs, example, daily completed order count of a driver. The latency to
• Near real-time (NRT) features are computed by a stream process, say a Kafka consumer of allocation events. For example, no. of orders accepted by a driver partner in the last hour. Latencies are in seconds. Features are fresh, can be used for online prediction.
• Real-time (RT) features are computed at the time of the prediction. Latencies are < 1 sec. Hard to scale, can be expensive to setup ingestion and complex derived features on top of real-time metrics/attributes.

What goes on inside a Feature Platform

This section unpacks the internal building blocks—feature registries, transformation engines, batch/stream processors, online stores, and materialization and orchestration layers. We’ll walk through how these components work together to ensure consistency, low-latency computation, inference.

Feature lifecycles and how to manage them