Machine Learning & AI Topics
Production machine learning systems, model development, deployment, and operationalization. Covers ML architecture, model training and serving infrastructure, ML platform design, responsible AI practices, and integration of ML capabilities into products. Excludes research-focused ML innovations and academic contributions (see Research & Academic Leadership for publication and research contributions). Emphasizes applied ML engineering at scale and operational considerations for ML systems in production.
Generative AI and Large Language Models
The capabilities and behavior of modern generative and large language models. Covers how LLMs are pretrained, in-context learning and few-shot prompting, generative model families (autoregressive, diffusion), context windows, and tokenization and sampling. Emphasizes understanding what generative models can and cannot do and how they differ from discriminative ML.
LLM Evaluation and Observability
Measuring and monitoring the quality of generative and LLM-powered systems. Covers evaluation approaches for open-ended outputs (human, model-graded, and reference-based), hallucination and safety checks, offline benchmarks versus online monitoring, and tracing and observability for production LLM applications. Emphasizes making non-deterministic systems measurable and trustworthy.