Machine-learning Services Architecture Decision Assistant Architect
A structured way to make and document architecture calls for machine-learning services. Built for agency dev shops working in machine-learning services, tuned for Claude. Fill in the placeholders, paste, and get a structured, ready-to-use result — with clear rules so the output stays specific to your situation instead of generic.
Preview
You are a pragmatic tech lead specialized in machine-learning services.
Inputs you will receive:
Stack: {{STACK}}
Constraints: {{CONSTRAINTS}}
Codebase context: {{CODEBASE_CONTEXT}}
… [purchase to unlock the full framework]Screenshots
Example outputs
Queue-based sync: +resilient to spikes / −adds 200-400ms p95 and a new infra dependency to own…
Version history
- v1Initial releaseJul 10, 2026
Buyers get every future update free.
Buyers also bought
Paste a slow query and schema — get back an optimized version, index recommendations, and a plain-English explanation of why it was slow.
Safely refactor gnarly legacy code: characterization tests first, seams identification, strangler-fig migration plan. Based on real enterprise migration experience.
A disciplined build-a-feature workflow for REST APIs: plan first, tests first, small diffs. Built for agency dev shops working in REST APIs, tuned for Cursor. Fill in the placeholders, paste, and get a structured, ready-to-use result — with clear rules so the output stays specific to your situation instead of generic.
A safe refactor plan for machine-learning services: characterization tests, seams, and steps that keep CI green. Built for platform teams working in machine-learning services, tuned for Windsurf. Fill in the placeholders, paste, and get a structured, ready-to-use result — with clear rules so the output stays specific to your situation instead of generic.
More from Petra Ito
Meaningful test coverage for serverless functions — behavior tests, edge cases and fixtures that stay maintainable. Built for startup engineering teams working in serverless functions, tuned for Bolt.new. Fill in the placeholders, paste, and get a structured, ready-to-use result — with clear rules so the output stays specific to your situation instead of generic.
A safe refactor plan for legacy Java systems: characterization tests, seams, and steps that keep CI green. Built for platform teams working in legacy Java systems, tuned for ChatGPT. Fill in the placeholders, paste, and get a structured, ready-to-use result — with clear rules so the output stays specific to your situation instead of generic.
A disciplined build-a-feature workflow for e-commerce stacks: plan first, tests first, small diffs. Built for agency dev shops working in e-commerce stacks, tuned for Claude. Fill in the placeholders, paste, and get a structured, ready-to-use result — with clear rules so the output stays specific to your situation instead of generic.
A systematic debugging workflow for data pipelines that finds root causes instead of symptoms. Built for solo developers working in data pipelines, tuned for ChatGPT. Fill in the placeholders, paste, and get a structured, ready-to-use result — with clear rules so the output stays specific to your situation instead of generic.
Reviews (3)
No reviews yet.