9 stories
NVIDIA is making the full NVIDIA Run:ai platform available as open source software, giving customers, developers, and ecosystem partners access to...
Susan Chang explains how Elastic transitioned from siloed, ad-hoc AI agent evaluations to a unified, production-grade framework. She discusses balancing LLM-as-a-judge with deterministic rules, bridging Python data science evals with TypeScript production code, and implementing deep tracing to…
Building an AI system takes much more than writing code. Traditional software often follows a straight path: plan, build, test, ship. AI projects are different.
Making Intent Routing Explainable: Calibrated Probabilities, Decision Records and a Four-Tier Cascade
When not Modeling is the Pragmatic Decision An anonymized forecasting case reconstructed with synthetic data: regime shifts, pretrained models and production judgment A few months ago I was involved …
MLOps Best Practices 2026 This article explores key concepts and practical implementations. Overview Detailed content coming soon. Published by Engr. Hamza, AI & MLOps Engineer
Detect hidden shifts in feature relationships with adversarial validation and scikit-learn The post How to Catch Data Drift When Every Feature Looks Normal appeared first on Towards Data Science.
Kubernetes manages what runs on your nodes. Managing the nodes themselves is the challenge: kernel settings, system packages, storage layouts, security agents,...
Mixture of experts (MoE) has become one of the defining architectural trends in large-scale AI model training. DeepSeek, Qwen, and Mixtral are examples of MoE...
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