Amazon Bedrock: A User’s Guide to benefits and utilization

Author:

Artur Haponik

CEO & Co-Founder

Reading time:

6 minutes

In recent years, generative AI has helped revolutionize various industries by providing valuable insights, boosting productivity, and enhancing user experiences. Unfortunately, developing, scaling, and deploying reliable generative AI applications is a complex and costly process. This is where Amazon Bedrock comes into play.

With Amazon Bedrock, you can easily experiment with top foundational models and customize them to execute tasks using your organization’s data sources, all without having to manage any infrastructure.

This post will provide an in-depth review of what Amazon Bedrock is, its functionality as well as its key characteristics.

What is Amazon Bedrock?

Amazon Bedrock is an AI platform from Amazon Web Services (AWS) that makes foundation models (FMs) from leading AI research companies like Amazon, Cohere, AI21 Labs, Anthropic, and Stability AI available for use through a unified API. [1] This means that developers do not have to build their own AI infrastructure to train, host, and deploy their generative AI applications.

Instead, they can rely on AWS’s cloud computing platform and large language models (LLMs) from leading AI research organizations to perform these tasks. Basically, Amazon Bedrock’s main goal is to simplify the creation and deployment of generative AI applications using foundation models and make the entire process more efficient.

You can think of Bedrock as a machine learning (ML) development platform that offers access to a wide variety of tools and specialized services, such as pre-built algorithms, colossal datasets, pre-trained models, comprehensive data management systems, and deployment infrastructure. Thanks to these tools and services, organizations are able to accelerate their AI projects without worrying about building their own infrastructure.

Currently, Amazon Bedrock’s biggest competitors include OpenAI’s ChatGPT, Claude, Dall-E 2, Streamlit, and Hugging Face. [2] Sometimes Bedrock is also compared with Amazon SageMaker, a fully managed service that provides the necessary tools to build, train, and deploy machine learning (ML) models. However, Amazon Bedrock is more focused on creating and deploying generative AI apps.

Understanding the functionality of Amazon Bedrock

Here is a step-by-step guide on how Amazon Bedrock works:

Choose a foundation model

As aforementioned, Amazon Bedrock provides software developers with access to a wide variety of foundation models through a serverless API. These FMs include Anthropic’s Claude, Meta’s Llama 2, AI21 Labs’ Jurassic, Cohere’s Command and Embed, Amazon’s Titan, and Stability AI’s Stable Diffusion.

Each of these foundation models comes with detailed descriptions, documentation, and sample outputs to help you choose the one that suits your task the most. Once you’ve selected the ideal foundation model for your specific use case and application requirements, you can access it through the single, unified API provided by Amazon Bedrock.

Experiment with foundation models (FMs) for different tasks

Bedrock allows you to experiment with different foundation models from different companies using interactive playgrounds for different modalities. You can filter these foundation models by categories such as text generation, code completion, image generation, and even language translation. The best thing about the playgrounds is that they allow you to experiment with different FMs for your specific application to get a feel of the model’s suitability for your preferred task.

Privately customize your foundation model with your data

Once you’ve selected your preferred foundation model, you can proceed to fine-tune it using your own private data to help enhance its performance and outputs. You can easily do this by uploading your private dataset and then configuring a fine-tuning job for your model. The main goal of customizing your foundation model using your private data is to ensure the generated outputs are relevant to your specific domain or use case.

Notably, the fine-tuning and continued pre-training of your chosen foundation model is done in an isolated and secure environment within the Amazon Bedrock platform. Most importantly, the private data you use to fine-tune your model is not used to train the original base models. Instead, this data is securely transferred through your Amazon Virtual Private Cloud (VPC) at all times. [3]

Build AI-powered agents

Next, you can proceed to build intelligent agents that can connect FMs with enterprise systems and data sources within the Bedrock framework. These agents will use your preferred foundation model as their core engine and access your other systems through secure connections.

Although this step is optional, having these agents allows you to automate repetitive and complex tasks for a foundation model without the need to manually write the code. By using agents to connect FMs with proprietary data sources, software developers can easily build applications that will produce up-to-date responses based on their own data.

Key characteristics of Amazon Bedrock

The following are some of the key features that make Amazon Bedrock stand out from its competitors:

Choice of foundation models

One of the best things about Amazon Bedrock is that it offers a wide selection of foundation models from leading AI research organizations to choose from. These models cater to different use cases and help organizations accelerate their AI projects. Most importantly, you can easily experiment with these foundation models to ensure you pick the one that aligns with your specific use case and requirements.

Seamless integration with Amazon Web Services (AWS)

Amazon Bedrock securely and seamlessly integrates with various AWS services you’re already familiar with to help create secure, reliable, and scalable generative AI applications. For example, Bedrock leverages Amazon CloudWatch for tracking metrics, Amazon S3 for data training and validation, and AWS Lambda for invoking actions.

Security and compliance

Generally, Amazon works with all foundation model vendors directly. This means that Amazon manages the foundation models on behalf of these organizations within their own ecosystem. Therefore, if you use one of these foundation models, all the data you upload will remain within Amazon Web Services (AWS) and will be protected as per Amazon’s data protection and safety standards. Currently, Amazon’s AWS supports over 100 data security and compliance certifications in an effort to help customers worldwide meet regulatory requirements. [4]

Customization

Bedrock allows developers to privately customize their preferred foundation models using their organization’s data using techniques such as fine-tuning and Retrieval Augmented Generation (RAG). [5] This feature ensures that the generative AI apps created are perfectly tailored to the needs and requirements of the specific organization or industry.

Final thoughts

Whether you’re a seasoned developer exploring generative AI or an entrepreneur eager to learn the technology’s potential for your business, Amazon Bedrock offers a great solution. Thanks to its scalable infrastructure, secure data management system, and pre-built algorithms, Bedrock makes it easy for organizations to harness the power of AI, thus opening doors for innovation and enhanced efficiency.

References

[1] Aws.amazon.com. What Are Foundation Models. URL: https://aws.amazon.com/what-is/foundation-models/. Accessed on January 29, 2024
[2] Saashub.com. Amazon Bedrock Alternatives. URL: https://www.saashub.com/amazon-bedrock-alternatives. Accessed on January 29, 2024
[3] Aws.amazon.com. What is Amazon VPC?. URL: https://docs.aws.amazon.com/vpc/latest/userguide/what-is-amazon-vpc.html. Accessed on January 29, 2024
[4] Pbs.org. Amazon Rolls Out Independent Cloud for Europe to Address Stricter Privacy Standards. URL: https://to.pbs.org/4bcVYNv. Accessed on January 29, 2024
[5] Aws.amazon.com. What Is RAG?. URL: https://cutt.ly/xwZYyED8. Accessed on January 29, 2024