By Anthony Loss, Director of Solution Strategy, ClearScale

Mainstream Generative AI has been one of the biggest stories of 2023. Thanks to advances in transformer and large language models, Generative AI is now broadly accessible. A subdomain of AI/ML technology, Generative AI enables users to prompt software programs with requests using natural language and receive back high-quality content in seconds. Teams are using Generative AI today for copywriting, virtual assistants, image generation, and more. The use cases will only expand as the technology improves, which is why now is the perfect time to learn how to use a tool like Amazon Bedrock from AWS.

Amazon Bedrock is AWS’ fully managed Generative AI service that streamlines the building and scaling of Generative AI applications. For existing AWS users, Amazon Bedrock is the fastest way to get up and running with Generative AI and integrate new applications within broader cloud environments. For non-AWS users, Amazon Bedrock could be the reason to finally migrate to AWS and offload cumbersome IT infrastructure management. Given its power and relevancy, understanding how Amazon Bedrock works will be valuable for years to come.

Amazon Bedrock Overview

One of Amazon Bedrock’s differentiating features is that it offers access to foundational models from today’s leading AI companies. Users can immediately leverage models from groups like A121 (Jurassic), Meta (LLaMa), and Amazon (Titan).

Normally, IT teams would have to interact with each of these models individually using separate APIs. With Amazon Bedrock, these foundational models are accessible through a single API. Users only have to change the input parameters to work with a different model. Over time, more and more foundational models will become available through Amazon Bedrock, making it the perfect platform for building a robust Generative AI practice.

The models accessible through Bedrock can be customized through a virtual interface without having to write any code. AI or cloud engineering teams can import company data directly into their foundational models from Amazon S3 and then fine-tune model performance. What’s more, these custom models are entirely private. In other words, companies can start with a foundational model, train with their own data, and retain fully proprietary models.

Amazon Bedrock also offers agents, which are managed services for configuring interactions with foundational models. Agents can break tasks down into discrete steps and orchestrate plans. This is perhaps one of Amazon Bedrock’s most exciting features because agents eliminate the need for system integration and infrastructure management. The key is knowing how to create agents that fulfill the unique Generative AI needs of the business.

Data Security

Another crucial Bedrock feature is you can configure any data used with the service to be secure in transit and at rest. Nothing gets shared with external parties or is used to tweak base models. Data sent to foundational models through Bedrock don’t have to traverse the public internet, which is the case when working with LLM APIs publicly.

Instead, Bedrock can be coupled with AWS PrivateLink to a private connection with foundational models inaccessible to the outside world. Of course, important AWS security and monitoring tools – AWS IAM, CloudTrail, and CloudWatch – also integrate with Bedrock. Furthermore, Amazon Bedrock has capabilities that make fulfilling GDPR and HIPAA compliance requirements easy.

The takeaway: Amazon Bedrock reduces risk while accelerating Generative AI innovation.

Prepare for Generative AI Use Cases with AWS and ClearScale

At ClearScale, we work closely with organizations across many industries that are exploring Generative AI applications and use cases. Through our work, we’ve discovered the biggest opportunities and pain points that come with incorporating Generative AI into existing workflows. That’s why we created our GenAI AppLink service.

Through GenAI AppLink, we help make the initial connection between applications and foundation models. We set up your environment notebooks, install required libraries, and create sample requests and responses so that you can begin to see your Generative AI vision take shape.

If you’re not ready to explore a specific Generative AI use case, we can also help you set up your data architecture and data engineering practices for Generative AI success on the cloud. After all, Generative AI is a form of AI/ML, which requires sophisticated data management skills and implementation.

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