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.
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.
Model Deployment and Inference Optimization
Serving trained models efficiently in production. Covers deployment and containerization, real-time and batch serving, latency budgets, throughput and cost optimization, quantization and model compression, and online/real-time learning constraints. Emphasizes meeting production performance targets without sacrificing model quality.
MLOps: Monitoring, Retraining, and Lifecycle Management
Operating machine learning systems reliably over time. Covers model and data monitoring, drift and degradation detection, feedback loops, retraining and model-freshness strategy, versioning and model registries, and pipeline and workflow orchestration. Focuses on keeping deployed models healthy and reproducible across their lifecycle.