MirahLabs Engineering Blog
Technical insights, architectural deep-dives, and system designs authored by our product engineers and AI research leads.
Recommender Systems: Collaborative Filtering to Deep Learning Architectures
Explore the evolution of recommender systems, from simple matrix factorization algorithms to deep neural networks like Wide & Deep and Two-Tower architectures.
Computer Vision with YOLO and PyTorch: From Training to Edge Deployment
Object detection with YOLO achieves real-time performance even on edge devices. Learn how to train custom YOLO models with PyTorch and deploy them to edge hardware using TensorRT and ONNX.
Quantization Techniques for LLMs: FP16 to INT4 and GPTQ
Deploying LLMs on local hardware requires massive memory footprint reductions. Learn how post-training quantization techniques like GPTQ and GGUF compress models from FP16 to INT4.
Understanding LLM Hallucinations: Causes, Detection, and Prevention
LLM hallucinations—confidently wrong answers—are the most critical reliability challenge in production AI. Learn why they happen, how to detect them, and architectural strategies to minimize them.
Deploying ML Models to Production: FastAPI + Docker + Kubernetes
Getting an ML model from Jupyter notebook to production requires API serving, containerization, and orchestration. This end-to-end guide covers model serving with FastAPI, containerization, and Kubernetes deployment.
Prompt Engineering: Advanced Techniques for Production LLM Applications
Prompt engineering is the art of communicating with LLMs effectively. Learn chain-of-thought prompting, few-shot examples, structured output, and prompt injection prevention for production systems.