ML & Computer Vision Specialist

About

This skill acts as a technical advisor for machine learning and computer vision projects, offering deep expertise in model selection, training pipelines, and inference optimization. It helps developers navigate complex decisions such as choosing between third-party APIs and self-hosted models, calculating break-even costs, and designing robust architectures for real-time video processing or RAG systems. By providing standardized frameworks and best practices for performance tuning and model monitoring, it ensures that AI-powered features are both scalable and cost-effective.

Key Features

• Detailed architectural patterns for synchronous, asynchronous, and edge inference
• Standardized monitoring protocols for tracking model drift and performance metrics
• Comparative analysis for API-based vs. self-hosted deployment strategies
• 32 GitHub stars
• Optimization techniques for GPU memory management and dynamic batching
• Decision frameworks for model selection across NLP, CV, and audio tasks

Use Cases

• Evaluating the cost-benefit of moving from proprietary APIs to self-hosted LLMs
• Implementing a Retrieval-Augmented Generation (RAG) system with optimal vector database selection
• Designing a high-performance object detection pipeline for real-time video