IntelliScale-ML-Engine is a production-grade framework designed to bridge the gap between complex ML models and seamless user experiences. It focuses on the operational excellence required to serve intelligent features to millions of users with sub-millisecond overhead.
- Asynchronous Inference Orchestration: Decouples API response times from model compute heavy-lifting, ensuring high system availability.
- Dynamic Request Batching: Automatically aggregates individual user requests into optimized batches to maximize GPU/TPU throughput.
- Context-Aware Personalization: Built-in middleware to inject real-time user context into model inputs for highly personalized results.
- Distributed Observability: Integrated telemetry using loguru and standard metrics for real-time monitoring of model health and latency.
mermaid graph TD A[Global Users] -->|Request| B(API Gateway - FastAPI) B --> C{IntelliScale Orchestrator} C -->|Queue & Batch| D[Model Worker Pool] D -->|Inference| E[Distributed Model Registry] C -->|Personalize| F[User Context Store] D -->|Result| C C -->|Intelligent Experience| B B -->|Response| A
-
Clone the Repo
οΏ½ash git clone https://github.com/deepthi-raghu/IntelliScale-ML-Engine.git cd IntelliScale-ML-Engine -
Install Dependencies
οΏ½ash pip install -r requirements.txt -
Run Ingress Service
οΏ½ash python main.py
Deepthi Raghu β Senior Machine Learning Engineer @ Apple. Dedicated to the intersection of scalable distributed systems and intelligent product experiences.
Building for scale. Designing for intelligence.