I design, build, and operate machine learning systems that serve customers at scale. Currently, I'm a Senior Applied Scientist at Amazon. Outside of work, I also...

• Write, speak, and prototype on ideas in machine learning, RecSys, and LLMs.
• Teach how to apply ML/LLMs effectively at ApplyingML.com & Applied-LLMs.org.
• Host a weekly AI Paper Club, monthly ML Meetups, an AI Job Board for execs/seniors.
• Send a newsletter on data, ML, LLMs, and what I'm learning to 8,800+ subscribers.

Get email updates

---

• 03 Nov 2024  ·  39 Lessons on Building ML Systems, Scaling, Execution, and More

• 27 Oct 2024  ·  AlignEval: Building an App to Make Evals Easy, Fun, and Automated

• 22 Sep 2024  ·  Weights & Biases LLM-Evaluator Hackathon - Hackathon Judge

• 08 Sep 2024  ·  Building the Same App Using Various Web Frameworks

• 18 Aug 2024  ·  Evaluating the Effectiveness of LLM-Evaluators (aka LLM-as-Judge)

• 07 Jul 2024  ·  How to Interview and Hire ML/AI Engineers

• 27 Jun 2024  ·  AIE World's Fair 2024 Keynote - What We Learned from a Year of LLMs

• 31 May 2024  ·  Netflix PRS 2024 - Applying LLMs to Recommendation Experiences

• 26 May 2024  ·  Prompting Fundamentals and How to Apply them Effectively

• 12 May 2024  ·  What We've Learned From A Year of Building with LLMs

• 12 May 2024  ·  What We've Learned From A Year of Building with LLMs

• 30 Jul 2023  ·  Patterns for Building LLM-based Systems & Products

• 11 Jun 2023  ·  Obsidian-Copilot: An Assistant for Writing & Reflecting

• 21 May 2023  ·  Some Intuition on Attention and the Transformer

• 14 Aug 2022  ·  Simplicity is An Advantage but Sadly Complexity Sells Better

• 31 Jul 2022  ·  Uncommon Uses of Python in Commonly Used Libraries

• 12 Jun 2022  ·  Design Patterns in Machine Learning Code and Systems

• 19 Sep 2021  ·  The First Rule of Machine Learning: Start without Machine Learning

• 27 Jun 2021  ·  System Design for Recommendations and Search

• 28 Feb 2021  ·  How to Write Better with The Why, What, How Framework

• 27 Jun 2024  ·  AIE World's Fair 2024 Keynote - What We Learned from a Year of LLMs

• 31 May 2024  ·  Netflix PRS 2024 - Applying LLMs to Recommendation Experiences

• 09 Oct 2023  ·  AI Engineer Summit 2023 Keynote - Building Blocks for LLM Systems

• 23 Sep 2022  ·  RecSys 2022 Keynote - Is the Juice Worth the Squeeze?

• 03 Oct 2019  ·  OLX Prod Tech 2019 Keynote - Asia's Tech Giants & SuperApps

• 04 Sep 2022  ·  Writing Robust Tests for Data & Machine Learning Pipelines

• 02 May 2021  ·  The Metagame of Applying Machine Learning

• 28 Feb 2021  ·  How to Write Better with The Why, What, How Framework

• 02 Aug 2020  ·  What I Did Not Learn About Writing In School

• 09 Jul 2020  ·  The 85% Rule: When Giving It Your 100% Gets You Less than 85%

192 posts, 29 talks, 16 prototypes, 381,436 words, and countless hours.

---

• • AlignEval: An App to Make Evals Easy, Fun, and Semi-Automated

• • AI Coach: Talk to Tara, an AI Coach, anytime at +1 (206) 558 8782

• • Obsidian-Copilot: A Prototype Assistant for Writing and Reflecting

• • LLM UXs: Interacting with LLMs with Minimal Chat

• • Raspberry-LLM: Dr. Seuss headlines, HackerNews trolls, etc.

• • LLM Experiments: LLM assistants, retrieval, and Discord

• • ApplyingML.com: Ghost knowledge of applying ML effectively

• • RecSys in PyTorch: Baselines + improvements via Graphs & NLP

• • Image Search: Image embeddings + cosine similarity

• • Image Classification: Transfer learning via Keras and Theano

• • Title Classification: Getting Amazon data, modeling, building a UI

• •

  applied-llms.org: Practical lessons from a year of building with LLMs

• •

  applyingml.com: Papers, guides, and interviews on how to apply ML effectively

• •

  applied-ml: Papers on real-world machine learning systems in industry

• •

  open-llms: Open large language models available for commercial use

• •

  llm-reading-list: Language modeling reading list (to start a paper club)

• •

  ml-design-docs: Template of design docs for machine learning systems

• •

  testing-ml: Example tests for machine learning systems

• •

  python-collab-template: Template with tests, type checks, linting, etc.

• •

  papermill-mlflow: Experimentation workflow for machine learning

• •

  1-on-1s: Questions to ask during 1-on-1s, from my time as a manager

• •

  omscs-faq: Georgia Tech Online Master's of Science in Computer Science

• •

  teardowns: Surveys & deep dives of data/machine learning systems