16

Computer Vision API

REST API for image classification and object detection using state-of-the-art deep learning models.

Key Features

  • Image classification with 95%+ accuracy
  • Object detection with bounding boxes
  • Face detection and recognition
  • OCR text extraction from images
  • Batch processing for multiple images
  • Real-time video stream analysis

Challenge

Optimizing model inference time for production workloads while maintaining accuracy and handling varying image sizes efficiently.

Solution

Implemented model quantization and TorchScript optimization, used dynamic batching, and built an efficient image preprocessing pipeline with GPU acceleration.

Technical Architecture

Frontend

Interactive API documentation with Swagger UI. Demo web interface for testing image uploads.

Backend

FastAPI for high-performance async REST API. PyTorch models with TorchServe for production inference. Redis for response caching.

Database

PostgreSQL for API usage tracking and model metadata. S3-compatible storage for processed images.

Deployment

Dockerized deployment on AWS with GPU instances. Auto-scaling based on request queue depth.

Development Process

Methodology

MLOps practices with model versioning and A/B testing for model updates.

Timeline

5 months: 2 months model training and fine-tuning, 2 months API development, 1 month optimization.

Team

Solo project with guidance from ML researchers.

Tools

PyTorch, FastAPI, Docker, NVIDIA CUDA, Weights & Biases for experiment tracking.

Performance & Analytics

Key Metrics

Sub-200ms inference time per image, 95%+ classification accuracy, 1000+ requests/minute capacity.

Optimization

Model pruning, INT8 quantization, GPU memory optimization, connection pooling.

Results

Model performance monitoring with accuracy drift detection and usage analytics.

Lessons Learned

  • Mastered PyTorch model optimization techniques
  • Learned production ML deployment best practices
  • Gained expertise in building high-performance APIs with FastAPI

Future Enhancements

  • Add custom model training API for fine-tuning
  • Implement edge deployment with ONNX
  • Add video analysis endpoints
  • Build model marketplace for pre-trained models