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.
Debugging and Testing ML Systems
Finding, diagnosing, and fixing problems in ML code, data, and models, and building tests that catch these problems before they reach users. Covers common ML pitfalls (data leakage, shape mismatches, silent training bugs, mis-specified loss or metrics), root-cause analysis of model regressions and production incidents (accuracy drops, calibration drift, intermittent or hard-to-reproduce failures), distributed-training-specific failures (multi-GPU divergence, intermittent OOM, precision-related instability), and the diagnostic tooling that supports it (reproducibility artifacts, structured logging, instrumentation). Also covers testing ML systems directly: unit tests for data and feature pipelines, validation checks for datasets and features, test oracles and acceptance criteria for probabilistic or non-deterministic model outputs, and integration and regression tests that catch model or pipeline regressions before deployment. Emphasizes the engineering rigor that keeps ML systems correct and maintainable.
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.
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.
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.