Machine Learning At Scale

Cadence

Weekly

Latest

2026-11-01

By

Ludovico Bessi

Based on

12 recent issues

• Covers: Production machine learning architectures, recommender systems, technical research paper breakdowns, and engineering career advice.
• For: Machine learning engineers, data scientists, and engineering leaders building real-world AI systems.
• Best for: Machine learning engineers seeking deep technical breakdowns of large-scale production systems.
• Skip if: Non-technical executives or beginners looking for basic AI prompt tutorials.

Start with

• Breaking the Speculative Decoding Trade-Off: How DSpark Solves Suffix Decay and Batch Waste: Explains how the DSpark framework improves speculative decoding performance and batch efficiency.
• Collapsing the Recommendation Funnel: Inside Kuaishou's OneRec: Breaks down an end-to-end generative recommendation architecture using Mixture-of-Experts models.
• Dissecting Coding Agent Harnesses: What Actually Drives Performance: Analyzes empirical benchmarks on coding agent scaffolds to reveal key performance drivers.

Visit

Summarised by AI from 12 recent issues. Audience size is not included. Prices are not included. Report a mistake.