Resources
Practical guides, benchmarks, and case studies for ML engineers and AI founders shipping production models.
A 40-page field guide for ML teams scaling data operations — schema design, IAA targets, vendor scoring, synthetic data tradeoffs, and a sample SOW you can fork.
12 min read
Request the playbookpages of field-tested guidance
real project SOW templates
evaluation rubrics included
industries deeply covered
Guide
Why label taxonomies break at scale, and a 6-step process for building one that doesn't.
Benchmark
We trained the same architecture on three data mixes. Results, charts, and recommendations inside.
Case Study
A walkthrough of the pipeline, controls, and SLA that delivered a production fraud dataset in 6 weeks.
Guide
Cohen's kappa, Krippendorff's alpha, and when to use which — with worked examples in Python.
Playbook
OCR, layout parsing, chunking, embedding selection, and evaluation — the full stack.
Guide
A practical flowchart for picking the right adaptation strategy based on data, latency, and budget.
No fluff, no sales blasts. Just the playbooks our engineers wish they had earlier.
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