Which tools are covered by this pattern?

The skill includes guidance for integration with message brokers like Kafka, stream processors like Apache Flink, and high-performance model servers such as TensorFlow Serving or ONNX Runtime.

How does it handle system overloads during traffic spikes?

It implements backpressure management, rate limiting, and graceful degradation strategies to ensure system stability when event rates exceed processing capacity.

Can I use this for batch processing tasks?

While some principles overlap, this pattern specifically targets unbounded, real-time data streams where latency is the primary constraint. For large-scale offline tasks, the Batch Serving Pattern is recommended.

What is the Streaming Inference skill used for?

It provides standardized patterns and implementation guidance for building machine learning systems that process continuous data streams and generate predictions in under 100 milliseconds.

Streaming Inference

Name: Streaming Inference
Author: lev-os

bylev-os

0•

Data Science & ML

Implements real-time machine learning architectures for processing unbounded data streams with sub-100ms prediction latency.

The Streaming Inference skill provides a comprehensive framework for architecting and deploying real-time machine learning systems that handle continuous data streams. It guides developers through the entire pipeline—from selecting message brokers like Kafka and Kinesis to implementing low-latency feature engineering and optimizing model serving using tools like ONNX or NVIDIA Triton. By incorporating strategies for backpressure handling, exactly-once semantics, and comprehensive health monitoring, this skill ensures robust, scalable inference for high-stakes applications such as fraud detection, real-time personalization, and IoT monitoring where milliseconds matter.

Key Features

01Implementation of exactly-once semantics and pipeline observability

02Real-time stream processing architecture design (Kafka, Kinesis, Pub/Sub)

030 GitHub stars

04Optimized model deployment for sub-100ms inference using ONNX and Triton

05Robust backpressure handling and flow control mechanisms

06Low-latency feature engineering and stateful aggregation patterns

Use Cases

01Predictive maintenance and sensor monitoring for industrial IoT environments

02Real-time fraud and anomaly detection for financial transactions

03Dynamic content ranking and personalization for social media feeds

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add lev-os/agents streaming-inference

For use in Claude.ai and ChatGPT

Download Skill