Insights & ideas
Insights and guides on AI and data.
AI agents in production: what deployment really takes
Autonomous agents promise a lot, but there is a large gap between a demo and production. We walk through what reliable deployment requires.
Read article →RAG or fine-tuning? How to choose correctly
The two most common ways to make a language model know your information are RAG and fine-tuning. We explain when each is worth it.
Read article →Data quality is the foundation of AI
AI projects usually fail not on models but on data. We explain why data quality is decisive and how to look after it.
Read article →MLOps in practice: how models stay reliable
Getting a model into production is the beginning, not the end. We walk through how MLOps keeps models reliable over time.
Read article →Vector databases: a practical guide
A vector database is the backbone of many AI solutions. We explain what it does, when you need one and how to choose the right one.
Read article →Managing the cost of AI
The cost of AI can spiral without you noticing. We walk through where the costs come from and how to keep them in check without sacrificing quality.
Read article →Computer vision at the edge
Computer vision does not always belong in the cloud. We explain when to run a model directly on the device and what it takes.
Read article →Evaluating large language models
How do you know which language model is best for your task? We walk through how to evaluate models objectively rather than by impression.
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