Why Generative AI on AWS and How it helps architects, developers to re-invent applications, code, customer experience and thought leadership

One of the latest advancements in AI is generative AI, earlier traditional AI trained on existing data sets, but generative AI can create new data that has never been seen before. Amazon web services released new suite of AI technologies including foundational large language models (LLMs) called Titan and a cloud computing service called Bedrock. Amazon Web Services (AWS) has designed its new AI technologies to help companies develop their own chatbots and image-generation services (such as OpenAI's DALL-E).

Customer demands led to develop large language models, as no single foundation model will cater to every use case. With Amazon Bedrock, Amazon provide access to many leading foundation models in a secure and integrated managed service.

Understanding of Generative AI: -

1. Generative AI is subfield of artificial intelligence. It refers to the artificial intelligence that can generate novel content.

2. AI that can produce original content which is close enough to human generated content for real world tasks.

3. We can customize with minimal fine tuning needed for specific domains.

4. We can develop multiple ML models with low cost and faster.

5. AI pre-trained on large sets of data known as FMs (Foundation Models)

Common Use Cases: -

1. Code Generation

2. Text Summarization

3. Question Answering

4. Chatbots

5. Search

Methods to use generative AI

1. Model Provider- Organizations who build their own foundation model from the scratch.
2. Model Tuners-Start with publicly and proprietary available foundation models and customize for their application domain.
3. Model Consumer- Make API calls to third part generative AI models for out of box?

Why Generative AI on AWS?

• Flexibility
• Secure Customization
• The most cost-effective infrastructure
• The easiest way to build with Foundation models
• Generative AI- powered solution

AWS native tools for architects and developers to build with generative AI: -

• Amazon Bedrock- Helps architects, developers, and customers to build solution without writing code and using API. The easiest way to build and scale generative AI applications with foundation models (FMs). Providing easy access to high-performing foundation models(FMs)for text and images from various vendors, including Amazon’s own Titan FMs. Integrating with various AWS tools such as Amazon SageMaker ML features, making it easy for developers to build, scale, and deploy generative AI applications without managing infrastructure.

AWS Bedrock is set to revolutionize the way enterprises use generative AI by making it more accessible and easier to leverage. Companies that already have their data and applications in AWS can now effortlessly integrate Bedrock to add generative AI capabilities without managing huge clusters of infrastructure or incurring significant costs.

AWS Bedrock currently supports below foundation models: -

1. Jurassic-2 from AI21 Labs, a multilingual LLM for generating text, translating languages, and answering questions informatively.

2. Claude from Anthropic, an LLM for text-processing and conversational tools.

3. Stable Diffusion from Stability AI, a text-to-image tool for generating unique, realistic, high-quality images, art, logos, and designs.

4. Amazon Titan, a family of foundation models developed by AWS for various text-related tasks such as text summarization, generation, classification, open-ended Q&A, information extraction, embeddings and search.

• AWS CodeWhisperer- AI coding companion which helps developers to build applications faster and securely. It is pre-trained on billions of lines of code and can generate code suggestions based on your comments and existing code. It streamlines coding processes and enhances productivity. Highlight its relevance for generative AI projects and provide practical examples. Refer the link https://aws.amazon.com/codewhisperer/.
• AWS released the general availability of Amazon EC2 Trn1n instances powered by AWS Trainium and Amazon EC2 Inf2 instances powered by AWS Inferentia2, the most cost-effective cloud infrastructure for generative AI
• Amazon Trainium- This is the compute used as ML accelerator. Amazon Elastic Compute Cloud (EC2) Trn1 instance deploys up to 16 AWS Trainium accelerators to deliver a high-performance, low-cost solution for deep learning (DL) training in the cloud. Refer the link for deep dive on this service https://aws.amazon.com/machine-learning/trainium/.
• AWS Neuron- AWS Neuron is the SDK used to run deep learning workloads on AWS Inferentia and AWS Trainium based instances. It supports customers in their end-to-end ML development lifecycle to build new models, train and optimize these models, and then deploy them for production.
• AWS SageMaker- Build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows. Refer the link https://aws.amazon.com/sagemaker/.

How it helps architects, developers for thought leadership

• With Generative AI on AWS, Architects and Developers can reinvent the applications.
• Can create new customer experience and transform the business.
• Design chatbots solutions with flexibility and secure customization.
• Design cost effective solutions.
• Many development teams are limited by fixed budgets, which puts a cap on the scope and frequency of training needed to improve their models and applications. AWS native generative AI services like Trainium based EC2 Trn1 instances solve this challenge by delivering faster time to train while offering up to 50% cost-to-train savings over comparable Amazon EC2 instances.
• Developers can generate code faster and with confidence.
• With generative AI, Architects can automate processes, gain insights from large data sets, and make more informed decisions.

Traditional ML vs Foundation Models

Generative AI industrial use cases