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.
Responsible AI: Fairness, Bias, and Interpretability
Building ML and AI systems that are fair, explainable, and safe. Covers identifying and mitigating bias, fairness metrics and tradeoffs, model interpretability and explainability techniques, label-bias feedback loops, and responsible and safe development practices for production models. Emphasizes accountability and transparency as first-class design constraints.
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 Evaluation and Validation
Measuring whether a model is good enough to trust and ship. Covers metric selection for classification, regression, and ranking (precision/recall, ROC-AUC, calibration, RMSE), offline validation design, evaluation-metric-to-business-objective alignment, and production safety guardrails. Emphasizes choosing metrics that reflect real objectives and avoiding misleading evaluations.
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.
ML Research to Production
Bridging novel research and shipped systems. Covers the research-to-production pipeline, staying current with emerging techniques, prototyping and validating novel algorithms or system designs, and the tradeoffs of adopting cutting-edge methods in a production setting. Emphasizes translating advanced or experimental work into reliable, shippable ML.
Machine Learning Fundamentals
Core concepts that underpin all machine learning work. Covers the difference between supervised, unsupervised, and reinforcement learning, the training/validation/test split, the learning objective, and how models generalize from data. Emphasizes conceptual clarity and knowing which learning paradigm fits a given problem rather than any single algorithm.
Prompt Engineering
Designing prompts and instructions that steer large language models toward reliable outputs. Covers zero- and few-shot prompting, chain-of-thought and structured reasoning prompts, system/role instructions, output formatting and constraints, and iterative prompt refinement. Focuses on getting consistent, controllable behavior from hosted and open models without changing their weights.
Applied ML Problem Framing and Tradeoffs
Turning an ambiguous real-world problem into a well-posed ML solution. Covers problem definition and objective specification, mapping business goals to a modeling objective, stakeholder and objective-function tradeoffs, computational feasibility and resource constraints, and walking through past ML projects and their decisions. Emphasizes judgment about whether and how ML applies before any modeling begins.
End-to-End ML System Design
Designing a complete machine learning system from problem to production. Covers the components and architecture of a production ML system, data flow from ingestion to serving, scalability, and integration of models into a larger product. Emphasizes the whole-system design tradeoffs that appear in ML system-design interviews.