AWS Brings Ease of AI App Development to Enterprises | Digital trends

By | August 14, 2023

The future of artificial intelligence is quickly becoming an out-of-box experience that businesses can customize based on their specific needs. Optimized chat experiences that are functional far beyond Q&A and tools to create AI applications without months of coding development could be the next step beyond introducing new plugins and extensions.

More common tools, such as ChatGPT for information and Midjourney for images, rely on public data and consistent developer coding to create a final product. Meanwhile, Amazon Web Services (AWS) is committed to making generative AI not only more productive and easier to navigate, but also data unique and data secure for the companies that deploy its tools.

Fionna Agomuoh / Digital Trends

The brand uses platforms like Amazon Bedrock to create a unique space for itself in the new AI market. Its flagship hub has been available since April and houses several of what it calls Foundation Models (FMs). AWS originally trained these base-level APIs and offers organizations the standard AI capabilities they want. Organizations can mix and match their preferred FMs and then continue to develop apps and add their own proprietary data for their unique needs.

“As a provider, we basically train these models on a large corpus of data. Once the model is trained, there is a cutoff point. For example, January 2023, the model does not have any information after that time, but companies want data that is private,” Amazon Bedrock Product and Engineering General Manager Atul Deo told Digital Trends.

Each company and the underlying models it uses will vary, so each resulting application will be unique based on the information organizations provide to a model. FMs are already basic templates. Then using open source information to populate the models can make applications repeatable across companies. AWS’s strategy allows companies to make their apps unique by introducing their own data.

“You would also like to be able to ask the model some questions and get answers, but if it can only answer questions about some outdated public data, it is not very useful. You want to be able to pass the relevant information to the model and get the relevant answers in real time. That is one of the core problems that it solves,” Deo added.

Foundation models

The several foundational models supported on Amazon Bedrock include Amazon Titan, as well as models from providers Anthropic, AI21Labs, and StabilityAI, each of which tackle important functions in the AI ​​field, from text analysis, image generation, and multilingual generation, among other tasks. Bedrock is a continuation of the pre-trained models AWS has already developed on its Stagemaker Jumpstart platform, which has been on the ground floor of many public FMs, including Meta AI, Hugging Face, LightOn, Databricks and Alexa.


AWS also recently announced new Bedrock models from the Cohere brand at their AWS Summit in late July in New York City. These models include Command, which is capable of summarizing, copywriting, dialog, text extraction and question answering for business applications, and Embed, which can perform cluster searches and classify tasks in over 100 languages.

AWS machine learning vice president Swami Sivasubramanian said during the summit’s keynote that FMs are low-cost, low-latency, intended to be customized privately, data encrypted, and not used to train the original base model developed by AWS.

The brand partners with a host of companies using Amazon Bedrock, including Chegg, Lonely Planet, Cimpress, Philips, IBM, Nexxiot, Neiman Marcus, Ryanair, Hellmann, WPS Office, Twilio, Bridgewater & Associates, Showpad, Coda and .

Agents for Amazon Bedrock

AWS also introduced the utility tool, Agents for Amazon Bedrock at its summit, which extends the functionality of basic models. Targeted at businesses for a wide range of use cases, Agents is an extended chat experience that helps users beyond standard chatbot questions and answers. It is able to proactively perform tasks based on the information it is fine-tuned to.

AWS Summit New York City 2023 – Keynote with Swami Sivasubramanian | AWS Events

AWS provided an example of how it works well in a commercial space. Let’s say a retail customer wanted to exchange a pair of shoes. By interacting with Agent, the user can indicate that they want to make a shoe exchange from size 8 to size 9. Agents will ask for their order ID. Once entered, agents will be able to access the retail inventory behind the scenes, tell the customer that their desired size is in stock and ask if they want to proceed with the exchange. When the user says yes, agents confirm that the order has been updated.

“Traditionally, it would be a lot of work to do this. The old chatbots were very rigid. If you said something here and there and it doesn’t work — you’d say, let me just talk to the human agent,” Deo said. “Now, because big language models have a much richer understanding of how people speak, they can take actions and make use of that proprietary data in a business.”

The brand also gave examples of how an insurance company can use agents to file and organize insurance claims. Agents can even assist company staff with tasks such as looking up the company’s PTO policy or actively scheduling time off, with a now-familiar style of AI prompt such as “Cone you file PTO for me?”

In particular, agents capture how foundational models allow users to focus on the aspects of AI most important to them. Without having to spend months developing and training one language model at a time, companies can spend more time fine-tuning information important to their organizations in Agents and ensuring it is up-to-date.

“You can fine-tune a model with your proprietary data. As the request is made, you want the latest and greatest,” Deo said.

As many companies in general continue to shift towards a more business-centric approach to AI, it appears that AWS’s goal is simply to help brands and organizations get their AI-integrated apps and services up and running faster. Cutting app development time could see a spring of new AI apps hit the market, but could also see many commonly used tools get much-needed updates.

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