About

About

I’m a product-focused ML Engineering Leader and bestselling author who specializes in building production machine learning systems that scale. As the first ML engineer at Digits, I established the technical foundation for machine learning initiatives ranging from similarity learning to custom generative AI solutions, working alongside an exceptional team of engineers and designers.

Core Expertise

My work centers on solving complex challenges in production machine learning:

  • Production ML Architecture - Designing scalable systems that bridge research and deployment
  • MLOps & Continuous Training - Building robust pipelines for model lifecycle management
  • Generative AI Systems - Implementing custom LLM solutions and deployment strategies
  • Similarity Learning - Developing sophisticated matching engines

Published Works

I’ve co-authored four machine learning books published by O’Reilly Media and Manning Publications:

GenAI Design Patterns (O’Reilly, 2025) A comprehensive guide to designing and deploying GenAI applications, with special focus on best practices and patterns.

Machine Learning Production Systems (O’Reilly, 2024) A comprehensive guide to engineering ML systems for production environments, with special focus on GenAI deployment patterns.

Building Machine Learning Pipelines (O’Reilly, 2020) The definitive resource for TensorFlow Extended (TFX) and end-to-end ML pipeline engineering.

NLP in Action (Manning, 2019) Practical guide to natural language processing, from statistical methods to deep learning approaches.

These publications represent my commitment to advancing the field and helping practitioners build effective ML systems.

Industry Leadership

Google Developer Advisory Board (gDAB) I advise Google on strategic AI/ML initiatives and provide technical guidance on developer experience and tooling.

TensorFlow Extended (TFX) Community Lead As a moderator of Google’s TFX Special Interest Group, I help shape the future of production ML tooling and foster community collaboration.

Open Source Contributor Active contributor to Google’s Model Card Toolkit and other ML infrastructure projects that help teams document and understand their models.

Professional Philosophy

I believe that machine learning’s true value emerges when research transitions successfully to production. My approach focuses on building systems that are not only technically sophisticated but also operationally robust, maintainable, and aligned with business objectives.

Through my work at Digits, publications, and community involvement, I continuously explore innovative approaches to ML engineering challenges while maintaining practical focus on real-world deployment requirements.

Resources