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Amazon's Bedrock: Unlocking the Gateway to Powerful Open LLMs
Amazon Bedrock offers unified API access to leading foundation models in the cloud, enabling organizations to build generative AI applications with ease, scalability, and cost-effectiveness.
To help customers stand up AI infrastructure quickly, Amazon introduced Amazon Bedrock, a fully managed service that provides organizations with unified API access to its AI platform and access to foundation models from leading AI providers. The service lets organizations use generative AI in the cloud without building their own infrastructure or paying for costly services. They can access pretrained foundation models from reliable model providers and enjoy a serverless experience.
Traditionally, building AI applications meant being locked into a specific AI technology or vendor. Bedrock’s ability to support various generative AI projects means CIOs have more options and can choose the best large language model (LLM) or AI tech for their specific needs. Amazon Bedrock introduced several new features recently in April and May of 2024. The key benefit that was announced is that along with Amazon’s own models, customers can import their custom LLMs or use an LLM from any provider.
Bedrock offers tools that assist organizations with different agents, retrieval augmented generation (RAG), and the Amazon Bedrock Studio.
- One API from the organization's infrastructure connects to Bedrock, where customers can use any LLM.
- Amazon Bedrock makes it easier and faster to compare and analyze AI models, so developers can focus more on creating new applications and experiences for the market.
- Data from customers is private and protected and not used to train the models or retained outside of the customer’s infrastructure.
- Amazon Bedrock enables responsible AI development with tools for customizing safeguards for different needs, helping AWS customers apply safeguards that suit their application needs and match their ethical AI policies.
- The Amazon Titan AI Model family expands with the new Amazon Titan Text Embeddings V2 model, which works well with RAG scenarios, and the Amazon Titan Image Generator, which can create or improve images cheaply with natural language prompts. The model can also spot watermarks, which helps customers check if an image came from the Amazon Titan Text Premier.
Amazon Bedrock
Image Credit: Amazon Web Services (AWS)
Our Take
AWS is carving out its niche as a comprehensive solution for AI, cloud computing, and data and software services, setting itself apart from rivals like Microsoft Azure and Google Cloud. The company is also becoming increasingly LLM agnostic, allowing customers to use their own models, models from any other provider, or AWS’s own LLMs.
As technology leaders consider platforms for making AI work, they should be aware that they could become locked into that framework. The architecture needs to be built with enough flexibility with different frameworks like Amazon Bedrock without vendor lock-in.
CIOs and CTOs evaluating large language models should explore Amazon Bedrock. The platform offers several advantages for organizations seeking versatile and scalable LLM solutions.
- Extensive Pretrained Model Catalog: Amazon Bedrock offers a wide range of pretrained LLMs from top AI companies. This lets you try out and use different models designed for specific tasks, possibly improving speed and quality.
- Easy Model Changing: The platform lets you change pretrained models within the Amazon Bedrock system. This flexibility can help you adjust your LLM usage to changing needs.
- Amazon Bedrock supports BYOLLM (Bring Your Own LLM): You can integrate your organization’s custom LLMs with Amazon Bedrock. This enables you to use your distinctive data and domain-specific expertise within a cloud infrastructure that can grow with your needs.
Amazon Bedrock presents a compelling option for organizations seeking a versatile and future-proof LLM strategy.