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Notes from production engagements — architecture decisions, tooling choices, and the tradeoffs behind each build.
How We Built a No-Code ML Pipeline with AWS — SageMaker Canvas + QuickSight on NASDAQ Data
Tech Stack Playbook built a no-code ML pipeline directly with AWS — S3 to SageMaker Canvas AutoML to QuickSight on a NASDAQ time-series dataset. Days, not mo...
Computer Vision for Competitive Tennis: A 33-Point Pose Pipeline That Runs on a Laptop
Pro tennis players have biomechanists and high-speed camera labs. Club-level competitors have their coach's iPhone. Tech Stack Playbook built a 6-stage compu...
The Model Is Not the Product: Shipping Multi-Model AI from Notebooks to Production on AWS
An executive advisory firm had proprietary, fine-tuned AI models trapped in Colab — manual, fragile, single-researcher. Tech Stack Playbook productionized th...
Against the Black Box: How We Engineered Transparent Health Scoring for Vitera
Whoop shows you a number. Oura shows you a ring. Neither will tell you why. Tech Stack Playbook engineered Vitera — an iOS health analytics platform with ful...
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