MirahLabs Engineering Blog
Technical insights, tutorials, and architectures written by our design and backend engineers.
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.
MLOps: Building Reproducible ML Pipelines with MLflow and DVC
Machine learning without MLOps produces science experiments, not production systems. Learn how MLflow tracks experiments and DVC versions datasets to build reproducible, deployable ML pipelines.
Ethical AI: Bias Detection, Fairness Metrics, and Responsible ML Deployment
AI systems can perpetuate and amplify societal biases. Learn how to audit models for bias, apply fairness constraints during training, and build responsible AI governance frameworks.
Feature Engineering for Machine Learning: From Raw Data to Model-Ready Features
Feature engineering is the most impactful step in the ML pipeline. Learn how to handle missing data, encode categoricals, create interaction features, and use automated feature selection.