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.
Model Training Infrastructure and Distributed Training
Scaling model training across hardware and time. Covers GPU/accelerator considerations, data and model parallelism, distributed and large-scale training, experiment tracking and training infrastructure, and the training-versus-inference compute tradeoff. Focuses on the systems and resource decisions that make large-model training feasible.
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.