Applied ML Notebook
Technical articles on applied ML and large-scale systems
Demystifying Entropy, Cross-Entropy, and KL Divergence in Modern Machine Learning
Entropy, cross-entropy, and KL divergence are key tools for reasoning about uncertainty in ML. This article unpacks these concepts and their practical significance for model training and evaluation.
Enhancing Concurrent Traffic Handling in Managed ML Services Using Batching-Ring Buffers
Managed Machine Learning (ML) services frequently face significant challenges when handling high-concurrency traffic. As the scale of operations grows—potentially serving thousands or millions of simultaneous requests—traditional approaches of sequential request processing or simple queuing mechanisms can lead to performance bottlenecks. This article explores how batching-ring buffers can be implemented to efficiently handle concurrent requests in ML services.