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What is Amazon Bedrock (AWS Bedrock)?

By Cameron Hashemi-Pour

Amazon Bedrock -- also known as AWS Bedrock -- is a machine learning platform used to build generative artificial intelligence (AI) applications on the Amazon Web Services cloud computing platform. Bedrock uses foundation models to simplify the creation of these apps and make the process more efficient.

Foundation models are adaptable AI models trained on large data sets to perform many kinds of tasks. They're versatile, reusable and don't require retraining for each new task. Bedrock replaces the physical infrastructure typically used to build generative AI apps with foundation models, simplifying the app building process.

Bedrock is a competitor to OpenAI ChatGPT and Dall-E 2. It is also compared to Amazon SageMaker, which is used to build and train complex machine learning models; Bedrock is more focused on building generative AI apps.

How Amazon Bedrock works

Amazon Bedrock gives software developers access to a wide range of foundation models from AI startups, such as AI21 Labs, Anthropic, Cohere and Stability AI, through a serverless application programming interface (API). For example, large language models such as Anthropic's Claude 2 and open source, text-to-image models such as Stability AI's Stable Diffusion XL can be used with Bedrock to simplify the delivery of generative AI apps.

On their own, foundation models are adept at comprehending natural language inputs and processing them to produce text or images as responses or outputs. However, they can't perform complex tasks or actions without direction.

AWS released Agents for Amazon Bedrock to designate and automate complex tasks for a model without requiring a developer to manually write the code needed to do so. Specifically, developers can use agents to connect foundation models to their proprietary data sources so the apps they build will produce up-to-date answers based on their own data. When a user employs a generative AI app built with Bedrock, an agent makes API calls that retrieve the data needed from proprietary sources to answer the user's requests or queries.

In addition to third-party foundation models, Amazon allows access to its own Titan foundation models, which include Titan Text to generate text and Titan Embeddings to translate textual inputs into numerical representations.

Applications developers can make with Amazon Bedrock

Amazon Bedrock can be used to build the following apps that are useful when applied to real-world use cases and workloads:

• Text generation. Apps built with Amazon Bedrock can generate original written text in various forms, such as short stories, blog posts, news articles and social media posts.
• Conversational AI. Customized chatbots or virtual assistants built using Bedrock are based on foundation models having access to proprietary data owned by a developer or software vendor. This provides good conversational AI responses to users' queries.
• Text summarization. A simple Bedrock app offers the capability to summarize text without forcing a user to pore over lengthy documents or materials.
• Image generation. Bedrock's API can interface with various types of foundation models, including those with text-to-speech AI capabilities. Given that, an app built on Bedrock can take a request or prompt for a specific image from a user and generate that image.

Learn more about generative AI and how it might change enterprise processes.

Generative AI landscape: Potential future trends

Generative AI in business: Fast uptake, earmarked funding

New skills in demand as generative AI reshapes tech roles

Generative AI tools to consider for marketing and sales

Will AI replace jobs? Job types that might be affected

Is Amazon Bedrock generally available?

Amazon Bedrock became generally available in September 2023. Two pricing plans are available. One plan lets users access foundation models on a pay-as-you-go basis. The other lets users provision throughput to meet their performance requirements, but it requires a time-based term commitment.

As businesses delve into generative AI and the myriad ways to customize generative AI applications, it's worth understanding the pros and cons of AI-generated content to be prepared for the future.