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The podcast explores the new AI app stack, covering topics such as model 'middleware', app orchestration, and emerging architectures for LLM applications. It discusses the misconception that large language models themselves are applications and explores the ecosystem of tooling and components surrounding them. The chapter also explores different categories of AI playgrounds, setting up the back end for testing products, and the components of the new generative AI stack. Key takeaways include the role of AI engineering and the elements of an AI stack infrastructure.
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Quick takeaways
- The AI app stack comprises three main components: application, data and resources, and models, emphasizing the need to consider the broader ecosystem beyond just the model itself.
- Effective AI engineering involves integrating various components like orchestration, middleware, caching, logging, and validation, leading to more robust and efficient AI applications.
Deep dives
Exploring the AI App Stack
The podcast episode delves into the emerging AI app stack, highlighting three main components: application, data and resources, and models. The application layer includes playgrounds where users can experiment with models, such as chatGPT or stable diffusion, often found on platforms like Hugging Face or AI Dungeon. The data and resources layer encompasses various tools and frameworks for hosting applications, such as cloud providers like AWS or third-party platforms like Versal. Finally, the model layer focuses on the orchestration, middleware, and validation surrounding the models. This includes logging, caching, and security measures to ensure reliable and secure model outputs.
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Discover
highlights
Listen to the best highlights from the podcasts you love and dive into the full episode