Build in Public
AWS AI Practitioner — Day 14: Final Review & Exam Prep
Consolidate learnings, take practice exams, and finalize readiness checklist.
Read article →Blog
Thoughts on AI-driven QA, BA workflows, and the practical lessons from building in public.
Build in Public
AWS AI Practitioner — Day 13: Mock Interview & Architecture
Practice explaining your end-to-end architecture, tradeoffs, monitoring, and rollback.
Read article →Build in Public
AWS AI Practitioner — Day 12: Project Polish
Finalize README, tests, and reproducibility notes for the mini-project.
Read article →Build in Public
AWS AI Practitioner — Day 11: Exam-style Review
Take timed practice exams and review explanations for incorrect answers.
Read article →Build in Public
AWS AI Practitioner — Day 10: CI & Deployment Pipelines
Add a smoke test to CI that calls your endpoint and practice deploy/rollback.
Read article →Build in Public
AWS AI Practitioner — Day 9: Security & Cost Estimation
Review encryption, IAM best practices, and estimate costs for endpoints.
Read article →Build in Public
AWS AI Practitioner — Day 8: Batch vs Real-time Patterns
Implement a small batch transform job and compare costs and latency vs real-time.
Read article →Build in Public
AWS AI Practitioner — Day 7: Midpoint Review & Quiz
Review week 1 results, identify gaps, and take a short practice quiz to guide week 2.
Read article →Build in Public
AWS AI Practitioner — Day 6: Automation & Reproducibility
Parameterize training, save run metadata, and script repeatable experiments.
Read article →Build in Public
AWS AI Practitioner — Day 5: Tests & Monitoring
Add input validation tests, CloudWatch metrics, and a basic alert for inference errors.
Read article →Build in Public
AWS AI Practitioner — Day 4: Deploy Inference
Deploy a model as a SageMaker endpoint or lightweight Lambda API and verify predictions.
Read article →Build in Public
AWS AI Practitioner — Day 3: Train a Baseline Model
Train a simple scikit-learn model in SageMaker or locally and save artifacts to S3.
Read article →Build in Public
AWS AI Practitioner — Day 2: Data Handling & IAM
Practice uploading, versioning data in S3 and set secure IAM roles for notebooks.
Read article →Build in Public
AWS AI Practitioner — Day 1: Environment & Core Concepts
Set up AWS CLI, S3, and SageMaker; review core ML concepts and service roles.
Read article →Build in Public
AI Transform + Speckit: Combine specs and in-app knowledge to deliver faster
How to use an AI-driven 'transform' workflow with Speckit to turn specs into implementable artifacts and ship product faster.
Read article →Build in Public
AWS AI Practitioner — Practical Study Guide
Practical hands-on study guide for AWS AI/ML practitioners: projects, checklist, and a 6-week roadmap.
Read article →Build in Public
Weekly Update — Aug 29, 2026: Reflecting and Resetting
A short reflection on the month: patterns that worked and the small resets I'll make next month.
Read article →Build in Public
Weekly Update — Aug 22, 2026: Cleaning the Inbox of Ideas
Notes on triaging ongoing ideas, pruning backlog, and keeping a small number of active experiments.
Read article →Build in Public
Weekly Update — Aug 15, 2026: Small Bets on Tooling
Reflections on small tooling bets that reduced friction and unlocked more time for thinking work.
Read article →Build in Public
Weekly Update — Aug 8, 2026: Tightening the Feedback Loop
Short notes on tightening feedback loops and small experiments that improved iteration speed.
Read article →Build in Public
Building a Local LLM Workflow on a Mac Mini
A build-in-public update on how we are shaping a practical local AI workflow for private, affordable, and useful automation.
Read article →Build in Public
This Week in AI QA: Building a Simpler Weekly Workflow
A practical look at how I used AI to streamline weekly QA and BA notes while keeping the work grounded in real context.
Read article →Build in Public
Hello World: Why I'm Building This Blog
I’m sharing how I use AI in QA and BA work, and why I’m documenting that journey in public.
Read article →