Practical resources for production-ready AI

A practical roadmap with milestones and deployment considerations (realistic, no hype).

Best practices for reliable AI operations: versioning, monitoring, releases, and governance.

Watch a practical discussion on vision projects in factories: data, evaluation, and deployment.

Technical deep dive into practical evaluation and optimization patterns for Thai language.

How to measure and communicate AI business value: a practical ROI checklist.

Guide to deploying AI models at the edge, including hardware constraints and optimization techniques.

Comprehensive checklist covering data readiness, team skills, infrastructure, and success criteria for AI projects.

A ready-to-use project template: scope, requirements, milestones, and deliverables.

A scorecard template to compare AI vendors: delivery risk, security, cost, and capability fit.

Assess if your organization is ready for RAG implementation: document quality, use cases, and infrastructure requirements.

A starter kit to evaluate RAG quality: datasets, scoring rubric, and regression checklist.

Best practices for collecting, labeling, and managing computer vision datasets for quality and production readiness.

Templates and guidelines for implementing AI-powered Statistical Process Control in manufacturing environments.

A practical cost framework for LLM systems in production: measurement and optimization levers (Thai language).

Best-practice security controls overview for AI services (no certification claims).

Operational checklist to align Legal/Compliance with Engineering (not legal advice).

Template for defining AI service SLOs/SLAs: latency, quality, uptime, and incident response.

Starter templates for model registry, monitoring dashboards, and CI/CD workflows.