ScaleInfer

Overview

ScaleInfer is a modular ML inference pipeline specifically designed for real-time recommendation systems. It provides a scalable, high-throughput architecture for serving recommendations with minimal latency, supporting complex feature engineering and model ensemble strategies.

Key Features

Modular Architecture: Composable pipeline components for flexibility
Real-Time Performance: Optimized for sub-100ms latency requirements
High Throughput: Handle thousands of requests per second
Feature Engineering: Integrated feature computation and caching
Model Ensemble: Support for multiple models and ensemble strategies
Distributed Serving: Horizontal scaling across multiple servers

Technical Implementation

Pipeline Components

Feature Fetcher: Efficient feature retrieval from stores
Feature Transformer: Real-time feature transformation
Model Scorer: Multi-model scoring and ranking
Ensemble Combiner: Intelligent ensemble aggregation
Response Builder: Efficient response formatting
Cache Manager: Smart caching for performance

Performance Optimization

Request batching
Feature caching strategies
Model quantization
Asynchronous processing
Connection pooling
Load balancing

Key Capabilities

Sub-100ms inference latency
Thousands of requests per second throughput
Complex feature engineering pipelines
Multi-model ensemble support
Distributed deployment
Real-time feature updates
Comprehensive monitoring
Graceful degradation

Code Repository

Explore the implementation on GitHub:

git clone https://github.com/Kernel-ML/scaleinfer.git
cd scaleinfer
pip install -e .
scaleinfer serve --config pipeline.yaml

Use Cases

E-commerce product recommendations
Content recommendation systems
Real-time personalization
Feed ranking
Search result ranking
Ads targeting

Future Enhancements

Advanced caching strategies
GPU acceleration support
Real-time model updates
A/B testing framework
Enhanced monitoring and analytics