3 AWS GenAI Solutions to fit your needs

Build The Ideal AI Environment with AWS GenAI Solutions

A couple of years ago, AI entered the mainstream. ChatGPT, a generative AI tool that seemed to have all the answers, was an immediate success. This sparked a surge in GenAI development, from plain ChatGPT API integrations to the accelerated development of Large Language Models. It all led to today’s ecosystem of GenAI-driven and enhanced products and platforms. These products and platforms enable you to build your own AWS GenAI Solutions on top of advanced large language models.

Running these language models requires a lot of computing power. With its massive global scale, cloud providers like AWS are the perfect place to leverage these models for your business needs.

AWS GenAI Services

AWS offers a variety of generative AI services to cater to different customer needs. Some customers want a ready-to-use chat assistant. Others need the quickest solution or want to use the biggest language models. Some may even need to train their own models using AWS GenAI Solutions. Let’s take a quick look at what AWS has to offer.

Amazon Bedrock

Amazon Bedrock is AWS’s service that enables customers to run many of the industry-leading Foundational Models. Think of LLama 3.1, Claude 3.5, AWS’ own Titan, and many more. Bedrock provides the freedom to select the most suitable model and use it privately. 

The best part of Bedrock? Customers do not have to worry about the underlying infrastructure to use these models. Setting this up from scratch on EC2 instances can be quite complex and time-consuming. You can even test out the capabilities of your chosen model in the AWS Bedrock Playground before using it in your GenAI application. This console playground lets you compare multiple models to see which works best for you.  

You can access these models using AWS’ SDKs and use them directly in your application for production use. You can also enhance them with your data using Request Augmented Generation using Bedrock Knowledge Bases, part of AWS GenAI Solutions.

Amazon Lex

A chatbot is one of the most common ways organisations use Generative AI. There are two options: you can create any custom application you want and use the features of AWS Bedrock. Alternatively, AWS GenAI Solutions offer a convenient service called Lex that makes publishing your first AI chatbot extremely simple. It even involves little to no coding knowledge. Lex now supports Bedrock’s Knowledge Bases, which enhances your chosen foundational model. This creates a full AI-driven bot suited to your business needs without manually configuring complex intents.  

Amazon SageMaker

Bedrock is one of the simplest ways to use the power of the best Foundational Models available in the market, powered by AWS GenAI Solutions. But in some cases, you might require more customisations than the default parameter tuning that Bedrock exposes. This is where SageMaker can come in handy.

SageMaker is a lower-level AWS GenAI solution that provides and runs machine-learning models on dedicated EC2 instances. However, the process is still greatly automated and fast-tracked, compared to deploying those models manually. Additionally, with SageMaker JumpStart you can have access not to tens (like in the case of Bedrock), but hundreds of models. They are maintained not only by AWS but other partners like Hugging Face.

Going further, SageMaker Jumpstart allows its users to train and fine-tune the already capable foundational models. You can adapt them even further to the required tasks. This offers several advantages. You can incorporate domain-specific knowledge. This allows you to achieve desired results with smaller, faster models. These models are also less resource-intensive. Additionally, you can tailor results without complex prompt engineering. Finally, you can adapt the model to your preferred style and tone.

Managing your GenAI infrastructure on AWS

We have mentioned a few of the many Generative AI services available in the AWS ecosystem. It is quite clear that the capabilities of taming AI for your business needs are there. And if needed, all this can be private, secured, and dedicated to your needs only. Bouygues Telecom already benefits from secure AWS GenAI Solutions. Read here how Devoteam and AWS brought generative AI to the TV screen.

Prior to jumping onto the train, of course, you should evaluate if your GenAI adoption journey is not just driven by the now two-year-long hype. But if the solution to your business problem at least partially is AWS GenAI Solutions, AWS is one of the best places to build on.

The examples we’ve reviewed are accessible. You don’t need a large team of data scientists or machine learning engineers to get started. However, things change in more advanced production scenarios, which require more effort. You need to design secure GenAI services, which also need to perform well and integrate with many different systems.

To help keep your AWS GenAI infrastructure and applications running smoothly and continue improving your AWS GenAI Solutions, Devoteam Managed Services offers proactive AWS support services. A certified AWS Managed Services Provider (MSP), it covers the reliability, governance, and security domains. Operating entirely from the European Union, Devoteam Managed Services ensures the highest level of AWS MSP services in compliance with your data governance requirements.