In the age of Artificial Intelligence (AI), we are living in a time when algorithms not only process data, but also have the ability to generate new information. Generative AI, which drives these creative machines, has dramatically expanded the boundaries of what is possible in content generation, unlocking highly relevant opportunities for both business and innovation.
Amazon has joined the wave of Generative AI with its AWS-hosted Bedrock platform, which offers the ability to access AI models developed by third parties, opening up new opportunities for companies looking to leverage this innovative technology.
What does Amazon Bedrock represent?
In the words of Adam Selipsky, CEO of AWS, the potential of Amazon Bedrock is to be considered as a reference hub to get the best out of every AI platform available.
Customers have the ability to use this platform to create and extend generative AI applications with a selection of the industry’s leading foundational models (FMs), through an API, with the guarantees provided by the AWS ecosystem as a secure environment and without the need to manage any infrastructure (serverless).
This tool opens a new door to democratization and access to generative AI. In this way, even customers who consider themselves in the early stages of technological transformation can see a great opportunity for the development of sophisticated products by reusing the available capacity of these models, which will allow them to innovate faster and easier through AI.
Some of the reference models already available on this cloud platform are: Claude, it is an alternative model to Chat GPT developed by Anthropic, Llama 2 model developed by Meta specially trained for dialogue tasks, or Stable Diffusion, capable of generating images from natural language developed by Stability AI, among others.
Amazon has also made a strong bet in this regard and provides its own set of foundational models to this select collection. This is Amazon Titan.
What makes Amazon Bedrock different?
The purpose of this platform is not just to offer a predefined language model or chatbot, but rather to make available diverse models provided by leading AI vendors, mostly successful startups, making them accessible through an API that enables acceleration in solving a wide range of problems.
Each foundational model contains detailed information on its applicability, associated code requirements, example prompts, and provides a limited but enlightening preview of how it behaves based on individual user prompts.
Users have the ability to develop agents for Amazon Bedrock to perform complex tasks and provide customized, up-to-date responses for their applications, all based on their own data.
Amazon’s contributions to Amazon Bedrock
Amazon Titan is a collection of foundational models pre-trained by AWS on massive datasets, making them versatile and powerful models designed to support a wide range of use cases. It is possible to use them “as-is” or privately customize them with proprietary data according to specific needs.
We could consider it as another alternative to models such as Claude or Chat GPT.
A particularly useful model is Amazon Titan Embeddings; it is able to convert text into numerical representations to feed RAG (Retrieval-Augmented Generation) use cases. This is currently a use case in high demand, combining the capabilities of retrieval-based models (designed to extract relevant information from a set of documents or a knowledge base) and generative models (which create new content from a prompt or context) to improve the quality and relevance of the generated text.
Undoubtedly, the availability of tools from the AWS ecosystem where Amazon Bedrock is fully integrated such as Sagemaker, with which technical teams are currently familiar and which are designed for training, deploying and monitoring AI models.
Other available tools such as S3 or Glue that provide scalable and durable storage for data, or the preparation and preprocessing of data for proper bias as input to these available models.
In short, Amazon has taken an important step to establish itself as a benchmark in this exciting race that generative AI promises to deliver in the near future. Its success will depend on its ability to persuade future ‘model providers’ to join this initiative, and it is something we will be watching closely.
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Data Scientist in Keepler Data Tech: "Live full, die empty" defines my state. This becomes my lifestyle taking me out of my comfort zone and driving my voracious learning attitude about different aspects of Data Science. I love learning by teaching and am always open to new challenges that push me further my comprehension."
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