Exploring the Layers of the AI Stack: From Data to Applications

The AI Stack: Navigating the Layers of Artificial Intelligence

Software development has always been compared to building a stack, with each layer representing a different component that comes together to create a functional application. But with the rise of artificial intelligence (AI), a new type of stack has emerged – the AI stack.

So, what exactly is inside an AI stack? According to industry experts, the AI stack consists of data sources, databases, integration tools, and the AI model itself. This infrastructure layer, model layer, and application layer work together to create AI-enabled applications with generative capabilities.

But navigating the AI stack isn’t easy. Developers need to consider factors like data processing, model training, and deployment solutions to build successful AI applications. Companies like MongoDB are addressing these challenges by providing unified databases that can handle both operational and vector data, simplifying the development process.

And as the AI stack continues to evolve, new tools and integrations are being developed to streamline workflows and improve productivity. Pinecone Connect, for example, allows developers to manage Pinecone resources directly from other platforms, making it easier to build knowledgeable AI applications.

But with the AI stack growing in complexity, the need for simplification and standardization is more important than ever. As the industry works towards creating a more streamlined AI stack, developers can look forward to a future where building AI applications is as easy as flipping pancakes on a Sunday morning.