14 stories
A new model called Jev is built for this kind of job. You give it the ticket and the possible answers, and it returns a choice with a probability, nothing else. A startup called TypeSafe AI released it on September 15.
Most machine learning portfolios stop at a notebook with 94% accuracy. Hiring managers have seen that a hundred times, and it says nothing about whether you can run a model in production. That gap is exactly what MLOps projects close.
Ask a judge model the same pairwise question 50 times and you’d expect 50 identical verdicts. In a 2026 study of GPT-4o-mini and GPT-4.1-mini as...
Nobody deployed anything. Error rates are flat, latency is fine, every request returns 200. But the answers your agent gave in March were better than...
The 11 best AI evaluation tools for production in 2026, ranked by seven criteria checked in vendor docs: online scoring, CI gates, alerts and self-hosting.
From guarding one agent to steering a fleet The post How to Govern AI Agents appeared first on Towards Data Science.
Fine-tuning teaches a small search agent your tools and environment, giving it the reliability of a frontier model at lower latency and cost. In this post, we fine-tune an LLM-powered search agent with multi-turn reinforcement learning (MTRL) on Amazon SageMaker AI and share the gains we measured…
The opportunity: Personalization as a revenue engineEvery second a shopper spends...
Building an agent is getting easier. More capable models and coding agents are making...
Your security team says traces can’t leave the building. Your finance team says the observability bill can’t grow with traffic. Your engineering team says they...
Compare 11 AI observability tools for LLM tracing and monitoring in 2026: tracing model, OpenTelemetry support, self-hosting, and which tools changed owners.
Learn how an MLOps pipeline manages data validation, feature engineering, training, evaluation, deployment, and production feedback loops.
The vast majority of teams working on large language models (LLMs) and machine learning (ML) systems diligently track hyperparameters.
LLM applications are everywhere now, and they’re fundamentally different from traditional software. They’re non-deterministic. They hallucinate. They can fail in ways that are hard to predict or reproduce (and sometimes hilarious).
Showing 14 of 14 stories
You're all caught up.
Get the top AI news and tools straight to your inbox every week.
Weekly · AI news
Join 15,000+ AI enthusiasts
