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1.1k 460
04 May, 2024
  Jupyter Notebook

What is Amazon Bedrock ?

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI. Using Amazon Bedrock, you can easily experiment with and evaluate top FMs for your use case, privately customize them with your data using techniques such as fine-tuning and Retrieval Augmented Generation (RAG), and build agents that execute tasks using your enterprise systems and data sources. Since Amazon Bedrock is serverless, you don’t have to manage any infrastructure, and you can securely integrate and deploy generative AI capabilities into your applications using the AWS services you are already familiar with.

Amazon Bedrock Features

Choose from a range of leading FMs Amazon Bedrock helps you rapidly adapt and take advantage of the latest generative AI innovations with easy access to a choice of high-performing FMs from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon. The single-API access of Amazon Bedrock, regardless of the models you choose, gives you the flexibility to use different FMs and upgrade to the latest model versions with minimal code changes.

Privately adapt models with your data Model customization helps you deliver differentiated and personalized user experiences. To customize models for specific tasks, you can privately fine-tune FMs using your own labeled datasets in just a few quick steps. Amazon Bedrock supports fine-tuning for Cohere Command, Meta Llama 2, Amazon Titan Text Lite and Express, Amazon Titan Multimodal Embeddings, and Amazon Titan Image Generator. To adapt Amazon Titan Text models to your industry and domain, you can use continued pretraining with unlabeled data. With fine-tuning and continued pretraining, Amazon Bedrock makes a separate copy of the base FM that is accessible only by you, and your data is not used to train the original base models.

Deliver more relevant FM responses To equip the FM with up-to-date proprietary information, organizations use RAG, a technique that involves fetching data from company data sources and enriching the prompt with that data to deliver more relevant and accurate responses. Knowledge Bases for Amazon Bedrock is a fully managed RAG capability that allows you to customize FM responses with contextual and relevant company data. Knowledge Bases for Amazon Bedrock automates the complete RAG workflow, including ingestion, retrieval, prompt augmentation, and citations, removing the need for you to write custom code to integrate data sources and manage queries.

Execute complex tasks across company systems Agents for Amazon Bedrock plan and execute multistep tasks using company systems and data sources—from answering customer questions about your product availability to taking their orders. With Amazon Bedrock, you can create an agent in just a few quick steps by first selecting an FM and providing it access to your enterprise systems, knowledge bases, and AWS Lambda functions to securely execute your APIs. An agent analyzes the user request and automatically calls the necessary APIs and data sources to fulfill the request. Agents for Amazon Bedrock offer enhanced security and privacy—no need for you to engineer prompts, manage session context, or manually orchestrate tasks.