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.
ML Algorithm Implementation and Numerical Considerations
Practical implementation details and algorithmic concerns when building machine learning systems. Topics include implementing gradient descent and variants such as stochastic and mini batch gradient descent, numerical stability and precision issues, vectorized matrix operations, efficient use of linear algebra libraries, feature normalization and standardization, distance metrics, algorithmic complexity, sorting and ranking techniques, back propagation implementation details, convergence criteria, initialization strategies, and performance trade offs for memory and compute. Also covers debugging model training, avoiding numerical overflow or underflow, and engineering considerations for productionizing ML algorithms.
AI System Scalability
Covers designing and operating machine learning systems to handle growth in data volume, model complexity, and traffic. Topics include distributed training strategies such as data parallelism, model parallelism, and pipeline parallelism; coordination and orchestration approaches like parameter servers, gradient aggregation, and framework tools such as PyTorch distributed, Horovod, and TensorFlow strategies; data pipeline and I O considerations including sharding, efficient formats, preprocessing bottlenecks, streaming and batch ingestion; serving and inference scaling including model sharding, batching for throughput, autoscaling, request routing, caching, and latency versus throughput tradeoffs. Also includes monitoring, profiling, checkpointing and recovery, reproducibility, cost and resource optimization, and common bottleneck analysis across network, storage, CPU preprocessing, and accelerator utilization.
Recommendation and Ranking Systems
Designing recommendation and ranking systems and personalization architectures covers algorithms, end to end system architecture, evaluation, and operational concerns for producing ranked item lists that meet business and user objectives. Core algorithmic approaches include collaborative filtering, content based filtering, hybrid methods, session based and sequence models, representation learning and embedding based retrieval, and learning to rank models such as gradient boosted trees and deep neural networks. At scale, common architectures use a two stage pipeline of candidate retrieval followed by a ranking stage, supported by approximate nearest neighbor indexes for retrieval and low latency model serving for ranking. Key engineering topics include feature engineering and feature freshness, offline batch pipelines and online incremental updates, feature stores, model training and deployment, caching and latency optimizations, throughput and cost trade offs, and monitoring and model governance. Evaluation spans offline metrics such as precision at k, recall at k, normalized discounted cumulative gain, calibration and bias checks, plus online metrics such as engagement, click through rate, conversion and revenue and longer term retention. Important product and research trade offs include accuracy versus diversity and novelty, fairness and bias mitigation, popularity bias and freshness, cold start for new users and items, exploration and exploitation strategies, multi objective optimization and business constraint balancing. Operational considerations for senior level roles include scaling to millions of users and items, experiment design and split testing, addressing feedback loops and data leakage, interpretability and explainability, privacy and data minimization, and aligning recommendation objectives to business goals.