"The AI stack has become increasingly confusing and complex. We've gone from two major players (OpenAI and Anthropic) in 2023 to over 200 providers, dozens of vector databases, and a constant stream of new "AI-native" tools launching weekly. AI applications are no longer in the experimental phase. These technologies have now matured to production-ready applications that enterprises can deploy at scale."
"The good news is that the chaos can be simplified. Underneath the hype, every production-grade AI application is built on the same foundation: a stack of layers that work together. In this article, we'll break down those layers, show how today's tools fit into each, and outline what matters when moving from a prototype to a production-ready AI system. You'll walk away with a clear mental model for:"
"Layer 1: Compute & foundational models This is the core of the modern AI stack, and contains the foundational models, or the "brain" of the AI system. It provides the essential infrastructure for model training, fine-tuning, and deployment. Compute providers: This includes cloud service providers like AWS, Google Cloud, and Azure, which offer access to powerful chips from companies like NVIDIA. These resources are essential for the computationally intensive process of training large models."
The AI landscape has expanded rapidly from a few vendors to hundreds of providers, many vector databases, and frequent new AI-native tools. Production-ready AI applications rest on a layered stack that unifies compute, foundational models, data handling, developer tooling, and deployment operations. Layer 1 supplies compute resources and pre-trained foundational models for training, fine-tuning, and inference. Clear mapping of tools to each layer and operational best practices enable transitions from prototypes to scalable enterprise systems. A layered mental model reduces chaos and clarifies what components are essential to build and scale AI solutions.
Read at LogRocket Blog
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