As AI continues to weave its magic into the fabric of the digital world, Amazon Web Services (AWS) has introduced its latest generative AI innovation: Amazon Bedrock. There is so much AI news, it is probably easy to miss Bedrock and what it means for Generative AI.
What is Amazon Bedrock?
Designed as a versatile AI platform, Amazon Bedrock enables AWS users to access AI models from a variety of providers, including AWS, via a user-friendly API. This new venture is focused on serving large customers who aim to construct enterprise-level AI applications, providing an edge over other AI model hosting services in terms of accessibility and efficiency.
Amazon Bedrock offers a diverse range of foundation models that empower customers to build custom generative AI applications. These models have been engineered to tackle tangible business challenges on a grand scale, ensuring that generative AI becomes a viable solution for a broad spectrum of applications. One of the main attractions of Amazon Bedrock is the Titan FM family, Amazon’s in-house solutions that include a text-generating model and an embedding model.
Foundational Models
A Foundational Model (FM) is a large-scale machine learning model that can be fine-tuned and adapted for a wide range of specific tasks. They form the “foundation” upon which various AI applications can be built.
These models are usually pre-trained on a vast amount of data, capturing patterns and structures from the training data that can be useful for many tasks. A good example is a large language model like GPT-3, which is trained on a wide variety of internet text. Once trained, it can generate human-like text, answer questions, translate languages, and even write code, among other tasks.
Foundational models can also be fine-tuned on specific tasks with a smaller amount of task-specific data. For example, a large language model can be fine-tuned to answer questions about a specific book by training it further on a question-answering dataset related to that book.
These models are “foundational” because they offer a starting point from which you can build various applications. Instead of training a model from scratch for each new task, you can start with a foundational model and adapt it to your needs, saving time, resources, and often achieving better performance.
The Titan FM text-generating model showcases capabilities such as blog post writing, summarizing documents, and extracting information from databases. The embedding model, on the other hand, processes text inputs and translates them into numerical representations that encapsulate the semantic meaning of the text. These models can be easily customized to cater to specific tasks and use cases, by merely directing the service to a handful of labeled examples.
The functionality of Amazon Bedrock extends beyond its foundation models. The platform also hosts a myriad of third-party models that bring a rich set of capabilities to the table. Take AI21 Labs’ Jurassic-2 family of models, which can generate text in multiple languages, or Anthropic’s Claude, known for its conversational and text-processing prowess. There’s also Stability AI’s suite of text-to-image models, perfect for generating images, artworks, logos, and more. The inclusion of these third-party models broadens the scope of Bedrock, giving users a diverse toolbox for their generative AI applications.
A distinguishing feature of Amazon Bedrock is the ease of customization it offers. AWS customers can fine-tune the foundation models for specific tasks by supplying labeled examples of the desired output, eliminating the need for a large volume of annotated data. Just about 20 labeled examples could be enough to start the customization process. Amazon promises that none of the customer’s data is used to train the underlying models. The models are fine-tuned using customer-provided examples, and the data remains encrypted within the customer’s Virtual Private Cloud (VPC).
The Amazon’s Titan FMs consist of two large language models (LLMs). The first Titan model is a generative LLM with capabilities like summarization, text generation, and open-ended Q&A. The second Titan model is an LLM that creates numerical representations or embeddings from text, making it ideal for personalization and search applications.
Amazon Bedrock’s models are well-equipped to detect and filter out harmful content from both inputs and outputs. This ensures responsible usage of the models across various applications. Amazon Bedrock makes it easier for customers to pick the right model for their needs, customize it with their datasets, and integrate it into their applications using AWS tools.
Data security and privacy are paramount to Amazon Bedrock. None of the customer data is used to train the underlying models, and it always remains encrypted within the customer’s VPC. Additionally, Bedrock includes features to detect and eliminate harmful content from the input data and the generated outputs, ensuring the responsible and safe use of generative AI technologies.
Amazon Bedrock is a dynamic tool for constructing generative AI applications. It offers a variety of customizable foundation models for different tasks and use cases. With a user-friendly interface and a diverse set of models, Bedrock streamlines the process of building and scaling generative AI applications. This opens up a realm of possibilities, allowing businesses to reimagine their applications and solve real-world challenges at scale using generative AI.