Providing the API layer and UI integration with fully Serverless and customizable LLMs.
ChatGPT and other generative AI applications have sparked the imagination of product teams across all industries. While a range of LLMs (Large Language Models) are emerging, and domain specific capabilities built either by third parties or internal training, there is still a step needed to embed generative AI experiences into applications and services.
The scale and abilities of Cloud providers, coupled with increases in data and recent AI breakthroughs, have enabled this recent wave of generative AI progress. To leverage these capabilities at scale a Cloud-First approach to host and scale these models is needed. Newer services like AWS Bedrock and others are enabling expansion of models while Serverless components are still needed to serve these capabilities to applications at scale. Building a scalable hosting and API interface, while embedding the user interfaces needed to leverage LLMs requires advanced cloud knowledge, innovative UI/UX approaches and a strong focus on data compliance and security.
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Model Hosting
Existing foundational models are customized with propriety application data to train powerful domain specific models. These are then be hosted at scale using Cloud-Native and Serverless services (e.g. AWS Bedrock, SageMaker and others).
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API Layer
To enable internal and third party applications to leverage the capabilities of LLMs (e.g. text generation, chatbots, image generation, search and more) an API layer needs to be built. Fully Serverless APIs provide the scale needed while integrating natively with Cloud LLM services. Websocket connections can be crucial for low latency chat interfaces, and Serverless services like AWS's AppSync can provide these with no management overhead.
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UI Integration
Once an API layer has been established connection and authentication with clear compliance logging is established. Innovative and extremely dynamic user interface components are then built and integrated to provide the end-to-end LLM application capability.
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