Code Review Practices for ML Codebases
Practical guide to code review for ML projects, covering what to look for in training code, data pipelines, serving code, and experiment …
Practical guide to code review for ML projects, covering what to look for in training code, data pipelines, serving code, and experiment …
A framework of best practices for IT service management, originally developed by the UK government.
A comprehensive standard published by PMI that defines project management processes, knowledge areas, and best practices.
What the twelve-factor methodology is, how it guides cloud-native application design, and which factors matter most in practice.
The AWS ML Lens extends the Well-Architected Framework to cover ML lifecycle phases, ML pipeline automation, model security, inference …
The Well-Architected pillar covering runbooks, automation, observability, incident response, and continuous improvement - and how it applies …
Practical prompt engineering patterns for production AI systems: system prompts, few-shot examples, chain-of-thought, structured output, …
What the Well-Architected Framework is, its origins at AWS, how Azure and GCP adopted it, its six pillars, and why it matters especially for …
How each of the 12 original 12-factor app principles applies to AI and LLM-based systems: model configuration, artifact management, vector …