ScaleStream-ML-Pipeline is a demonstration of how advanced software engineering principles are applied to machine learning services. Built for high-scale environments, this pipeline focuses on decoupling request handling from model inference to maximize throughput and minimize latency.
- Asynchronous Inference Orchestration: Utilizing Python's �syncio to manage concurrent requests without blocking the event loop.
- Dynamic Batching Logic: Automatically aggregates incoming inference requests into optimal batch sizes for hardware acceleration.
- Observability & Traceability: Production-grade structured logging with loguru for auditing every step of the inference lifecycle.
- Type-Safe Interfaces: Pydantic-driven request/response schemas to ensure data integrity across microservices.
mermaid graph LR A[Client Request] --> B(API Gateway - FastAPI) B --> C{Async Queue} C -->|Dynamic Batching| D[ML Inference Engine] D -->|Prediction| C C -->|Response| B B --> A
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Clone the Repo
�ash git clone https://github.com/shravan-narayan12/ScaleStream-ML-Pipeline.git cd ScaleStream-ML-Pipeline -
Install Dependencies
�ash pip install -r requirements.txt -
Run the Service
�ash python main.py
Shravan Narayan — AI/ML Software Engineer @ Apple. Specialized in building intelligent services that power hardware engineering workflows.
Clean Code. Scalable Models. Intelligent Experiences.