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AI stack mapping: foundational models to production deployment

By PulseAugur Editorial · [1 sources] ·

An AI stack can be effectively mapped by organizing vendors into distinct layers, starting with foundational models and labs. This approach helps in selecting the right model based on capability and cost, considering the Pareto frontier of performance versus price. Following this, agent frameworks and orchestration tools should be chosen based on how workflows chain together and state is managed. Grounding and retrieval layers involve data, vector databases, and knowledge graphs to provide context to models, with graph RAG being a complementary approach. Finally, layers for evaluation, observability, reliability, compute infrastructure, and developer tools ensure trustworthy AI deployment in production. AI

IMPACT Provides a framework for organizing and selecting AI vendors based on functional layers, aiding in system design and procurement.

RANK_REASON The item provides an opinionated guide on how to architect AI systems, rather than announcing a new product or research finding.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI stack mapping: foundational models to production deployment

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The item provides an opinionated guide on how to architect AI systems, rather than announcing a new product or research finding.
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Conor Bronsdon ·

    Map your AI stack by layer before you pick vendors

    <p>It is tempting to start with a model name or a hot framework. I think that order is backwards. I'd start by drawing your stack in layers, the same way the Chain of Thought ecosystem map groups the companies guests represent on the show. Each layer answers a different question,…

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