Machine Learning Engineer

Optimizing Neural Networks for Real-Time Solutions

Project Overview

I collaborated on an exciting project to enhance the performance of a neural network designed for real-time HTTP request interception at scale. The project was for a high-profile client, The Disney Company, where the focus was on creating an embedding generator optimized for speed and accuracy.

Quick Overview

• Optimized neural networks for real-time HTTP request interception.
• Experimented with shallow neural network architectures (2-5 layers).
• Balanced precision, recall, and latency for production deployment.
• Used tools like Google Colab and TensorFlow for testing and training.

Technical Approach

• Neural Network Architecture: Experimented with different depths for optimal performance.
• Performance Metrics: Analyzed precision, recall, and latency trade-offs.
• Environment: Leveraged Google Colab for rapid experimentation.

Skills and Tools

• Frameworks: TensorFlow, PyTorch
• Techniques: Deep learning architecture optimization, performance evaluation
• Deployment Awareness: Designed with production constraints in mind