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.
LLM Evaluation and Observability
Measuring, monitoring and releasing generative and LLM-powered systems. Covers evaluation of open-ended outputs (human review, calibrated model-graded judges, reference-based and embedding scores, perplexity), hallucination, toxicity, privacy-leak and red-team checks, offline benchmarks versus online monitoring, and production observability (logs, traces, metrics, cost and token attribution, privacy-safe telemetry, drift and quality alerts). Also release decisions: A/B tests, canary rollouts, SLOs and go or no-go gates. Building the grounding architecture itself, calibration of predictive models and general software testing are covered elsewhere.
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, controllable outputs without changing model weights. Covers zero- and few-shot prompting and example selection, chain-of-thought and step-by-step reasoning prompts, system, developer and role instructions and instruction hierarchy, output formatting and structured output (JSON schemas, templates, constraints), task decomposition and prompt chaining, managing context and token budgets inside a prompt, prompt injection as a design constraint, and iterating on, versioning and regression-testing prompts against a test set. Also covers prompt-level ways to reduce hallucination (grounding in supplied text, quoting support, allowing abstention) and unsafe, toxic or off-policy output (scoped roles, refusal templates), and prompting a model to generate constrained, validated output such as SQL. Boundary: excludes how models are pretrained and behave internally, sampling and decoding internals, fine-tuning, retrieval pipelines, and general LLM evaluation and monitoring.
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.
LLM Fine-Tuning and Alignment
Adapting foundation models to specific tasks and desired behavior. Covers transfer learning and using pretrained models, full and parameter-efficient fine-tuning, instruction tuning, and alignment methods such as RLHF and preference optimization. Focuses on when and how to customize a base model rather than prompt it, and the data and compute tradeoffs involved.
Recommendation, Ranking, and Personalization
Systems that select and order items for users. Covers candidate generation and ranking (two-stage retrieval, embedding-based retrieval with two-tower models and approximate nearest-neighbour candidate lookup, multi-stage scoring and cross-encoder re-ranking), collaborative and content-based filtering, matrix factorization (alternating least squares, Bayesian personalized ranking), sequence-aware and graph-based recommenders, cold start, learning-to-rank and negative sampling, turning noisy implicit feedback into labels, position, exposure and popularity bias and feedback loops, diversity, fairness-aware exposure and multi-objective re-ranking, counterfactual (off-policy) evaluation of recommenders (inverse propensity scoring, doubly robust estimators), and exploration with multi-armed and contextual bandits (epsilon-greedy, upper-confidence-bound and Thompson sampling, LinUCB, regret, non-stationarity, constraints, rollout, and when a bandit beats a fixed split or full reinforcement learning). Includes end-to-end recommender and personalized feed design. Focuses on the modeling and evaluation patterns specific to recommendation and ranking at scale. General A/B test design, generic ranking-metric definitions and ML serving infrastructure are covered elsewhere.