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Stop Optimizing RMSE Alone: How to Evaluate Scientific AI Systems for Real Decision Value > Perspective: I am bullish on ML for science, but skeptical of benchmark-driven claims that ignore physical validity and downstream decision impact. In scientific workflows, a model is useful only if it is...
Differentiable Simulators + Foundation Models: A Practical Stack for Learning-Accelerated Domain Science > Perspective: I favor hybrid systems that combine first-principles simulators with machine learning, rather than replacing physics with black-box models. In scientific settings, reliability and...
Building Effective AI Agents: Core Principles and Design Patterns AI agents represent a fundamental shift in how we interact with artificial intelligence. Rather than simple query-response systems, agents can reason, plan, use tools, and accomplish complex multi-step tasks autonomously. This...
Large pre-trained word embeddings are a cornerstone of modern NLP, but their memory footprint is a real deployment bottleneck. A standard 300-dimensional GloVe vocabulary of 2 million tokens consumes ~2.4 GB in float32. On mobile devices, edge hardware, or in multi-model serving environments, this...
An AI researcher documents replacing her production ML pipeline with open-source alternatives. Inference costs dropped 94% while quality decreased only 3%. Includes exact benchmarks, cost tables, and honest assessment of tradeoffs.
A practical guide to ML model versioning using MLflow, covering the Model Registry, lifecycle management, and deployment.