Introducing Amazon Bedrock – AWS response for OpenAI’s DALL·E 2, ChatGPT-4 and other Generative AI Rivals

There’s no denying that all major tech companies now are on their toes, trying to grab enough Artificial Intelligence (AI) market share which has been exponentially growing for the past several years. The race has been further highlighted when OpenAI released its game-changing ChatGPT service which spread like wildfire for both geeks and non-IT professionals alike.  Based on various reports, it is set to boom into a $90 billion industry by 2025 or even more.

What is Amazon Bedrock?

Amazon Bedrock is the latest machine learning platform that was released by Amazon Web Services(AWS) that enables users an easy way to build and scale generative AI applications with foundation models, or “FMs” for short. This service accelerates the development of generative AI applications using foundational models without managing any physical infrastructure or the high operational overhead that usually comes with it. You can choose from different foundation models to cater for your particular use case such as from AI21 Labs, Anthropic, Stability AI, or internally from AWS. You can privately customize FMs using your organization’s data and utilize AWS tools and capabilities that you are familiar with, which empower you to deploy scalable, reliable as well as highly secure generative AI applications.

So what can Amazon Bedrock do?

Amazon Bedrock is a machine learning platform that is similar with Amazon SageMaker. Both of the services have multiple sub-components/services, but the latter is more complicated and is primarily used by Machine Learning Engineers to build and train custom machine learning models. This is different from Amazon Bedrock which is meant to be a more user-friendly platform for building and scaling generative AI applications through the use of foundation models (FMs).

Amazon Bedrock can be used in various use cases such as:

• Text generation
  • You can generate new pieces of original written content like short stories, social media posts, articles, web page copy, or even school essays.

• Chatbots
  • Amazon Bedrock is capable of building conversational interfaces like chatbots and virtual assistants to improve the user experience for your clients. It is possible that this platform can provide a direct integration with Amazon Lex (a chatbot service in AWS).

• Search
  • This service will allow its users to easily search, find or even synthesize information to quickly answer various questions from a large collection of diverse data.

• Text summarization
  • You can use this service to summarize textual content – from blog posts, essays books, documents and others to get a concise summary of the subject matter – removing the need to read the full content in verbose.

• Image generation
  • Amazon Bedrock can create a realistic and artistic picture of different subjects, environments, and scenes that you specify. 

• Personalization
  • This AI service can also personalize the way you deal with your customer – allowing your users to search exactly what they’re looking for with more relevant and contextual product recommendations, which is way better than just mere keyword matching.

What the heck is a Foundation Model (FM)? 

Based on this Stanford article, Foundational models are defined as…

models trained on broad data that can be adapted to a wide range of downstream tasks.

In other words, it is a “foundational” model that can be trained and used for a variety of different tasks in contrast to crafting a highly customized Machine Learning model for a particular use case only. It promotes collaboration and unity among various contributors so a single model can be re-used and utilized more efficiently. So instead of spending thousands of hours training a custom model that does exactly one thing, you build a more adaptable and versatile model that can do a plethora of tasks.

Here’s another definition from Standford University’s Center for Research on Foundation Models (CRFM):

In recent years, a new successful paradigm for building AI systems has emerged: Train one model on a huge amount of data and adapt it to many applications. We call such a model a foundation model.

I also recommend watching this YouTube series if you want to deep dive into this topic:

How can I access and try the new Amazon Bedrock service?

To date, the Amazon Bedrock service is not yet available in the AWS Management Console. However, you can express your interest by filling out the form here: https://pages.awscloud.com/generative-AI-interest-learn.html

Take note that you will need an AWS Account ID in order to sign up for service updates. 

Related Resources about Amazon Bedrock

https://aws.amazon.com/bedrock/

https://aws.amazon.com/blogs/machine-learning/announcing-new-tools-for-building-with-generative-ai-on-aws/ 

https://aws.amazon.com/bedrock/titan/

You can also check out this informative discussion between Dr. Werner Vogels, Amazon CTO and Swami Sivasubramanian (AWS VP of database, analytics, and ML) talking about the broad landscape of generative AI. They discussed the reasons why it’s not hype, and how Amazon Web Services (AWS) is democratizing access to large language and foundation models.

Free Resources to Learn AWS Machine Learning 

You can check out this collection of free Machine Learning courses to start your ML journey, which includes the following:

• Math for Machine Learning
• Computer Vision with GluonCV
• Exam Readiness: AWS Certified Machine Learning – Specialty (MLS-C01)
• Building a Machine Learning-Ready Organization
• …and many more