#Machine-Learning
6 posts in tag “Machine-Learning”.
Inside Vector Databases: Building Retrieval-Augmented Systems that Scale
2025-10-26How modern vector databases ingest, index, and serve embeddings for production retrieval-augmented generation systems without falling over.
Learned Indexes: When Models Replace B‑Trees
2025-10-04A practitioner's guide to learned indexes: how they work, when they beat classic data structures, and what it takes to ship them without getting paged.
The Johnson-Lindenstrauss Lemma and the Geometry of High-Dimensional Data
2025-09-05Explore the surprising geometry of high-dimensional spaces: the Johnson-Lindenstrauss lemma showing that random projections preserve pairwise distances, the concentration phenomena that make it work, and its profound applications in nearest-neighbor search, compressed sensing, and machine learning.
Latency-Aware Edge Inference Platforms: Engineering Consistent AI Experiences
2023-03-12A full-stack guide to designing, deploying, and operating low-latency edge inference systems that stay predictable under real-world constraints.
Keeping the Model Awake: Building a Self-Healing ML Inference Platform
2023-02-14A field report on taming production machine learning inference with proactive healing, adaptive scaling, and human empathy.
Convex Optimization: Gradient Descent, Nesterov Acceleration, KKT Conditions, and the ML Stack
2020-02-18A deep investigation of convex optimization—the engine of modern machine learning—from gradient descent and Nesterov momentum to KKT conditions and interior-point methods.