Development

Scalable Architecture for Real-Time ML Systems

Ayush Anand
Ayush AnandPrincipal AI Consultant
•
Aug 24, 2025
•
9 min read
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Deep dive into feature stores, container orchestration, and message queues required to maintain low-latency inference at scale.

Maintaining machine learning inference pipelines at scale requires specialized infrastructure patterns that handle heavy stream data processing and low-latency response loops.

Core Infrastructure Pillars

  1. Feature Stores: Centralize feature definitions to eliminate training-serving skew.
  2. Dynamic Scaling: Scale inference nodes dynamically via Kubernetes configurations during peak usage hours.
  3. Event Stream Buffering: Use message brokers (like Apache Kafka) to buffer incoming queries and prevent system bottlenecks.
Development
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Ayush Anand

Ayush Anand

Principal AI Consultant

Ayush is a Principal AI Consultant and Engineer at Xorblin, specializing in designing autonomous ecosystems, agentic workflows, and scalable data architectures.

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