ML Architecture Blog | mlai.qa — Insights for AI Startups
ML architecture insights, MLOps patterns, and data pipeline design guides for Series A–C AI startups. Practical advice from the mlai.qa team.
When to Build vs Buy Your ML Infrastructure
A framework for deciding when to build ML infrastructure from scratch vs. use managed services — with a decision matrix …
The ML Architecture Review: 20 Things We Check
The complete checklist we use in our ML architecture reviews — training infrastructure, data pipelines, model serving, …
Model Monitoring vs Observability: What ML Startups Get Wrong
The difference between monitoring and observability in ML systems — what to instrument, which tools to use, and the …
MLOps Stack Comparison: Kubeflow vs Metaflow vs Prefect
An honest comparison of the three most popular MLOps frameworks for AI startups — when to use each, setup complexity, …
ML Platform Engineering: What It Is and When You Need It
A practical guide to ML platform engineering — what it covers, when startups need it, and how to build a serving and …
ML Architecture Mistakes That Kill Series B Due Diligence
The 5 ML architecture decisions that Series B investors flag in technical due diligence — and how to fix them before …
Fine-Tuning vs RAG: How to Choose for Your AI Product
A practical decision framework for choosing between fine-tuning and retrieval-augmented generation — with cost, latency, …
Data Pipeline Architecture for Real-Time ML
Architecture patterns for building real-time ML data pipelines — streaming vs batch, feature store design, and the tools …