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Things we've written, tools we've built, and stuff we find useful.

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Guides & Playbooks

Practical resources for AI projects. What we've learned from 100+ implementations.

ML Project Checklist

A practical checklist for planning and executing ML projects. Based on what we've learned from 100+ projects.

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Data Readiness Assessment

Questions to ask before starting an AI project. Save yourself time by identifying issues early.

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Production ML Checklist

What you need to think about before putting a model in production. Monitoring, fallbacks, A/B testing.

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LLM Evaluation Framework

How to evaluate whether an LLM is right for your use case and which one to pick.

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Community

Open Source

Some tools we've built and shared with the community.

model-monitor

Lightweight ML model monitoring. Track drift, latency, and errors with minimal setup.

2.3k

data-validator

Schema validation and data quality checks for ML pipelines.

1.1k

llm-tester

Automated testing framework for LLM applications. Catch regressions before users do.

890
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Recommended Reading

Books, papers, and blogs we've learned from.

Books

  • Designing Machine Learning Systems

    by Chip Huyen

  • Machine Learning Engineering

    by Andriy Burkov

  • Building ML Powered Applications

    by Emmanuel Godsey

  • Reliable Machine Learning

    by Sculley et al.

Blogs & Newsletters

  • The Gradient

    ML research and perspectives

  • Machine Learning Ops Newsletter

    Weekly MLOps insights

  • Sebastian Ruder's Blog

    NLP and transfer learning

  • Lil'Log

    ML concepts explained clearly

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