Fundamentals of Generative AI for Beginners - Coursera Course
This free course is designed for students, early career professionals, and non-ML practitioners who want to get started with Generative AI.
Generative AI - A Primer
Generative AI is everywhere now-a-days. But what is it? Well, think of it as a type of AI that can create new content an ideas, including conversations, stories, images, videos, and music. For your gen AI journey with AWS, this video gives a foundational overview of where it came from, where it’s going, how it works, and how to get started.
Build your Generative AI Applications on AWS
To build generative AI applications on AWS, you can start with Amazon Bedrock to choose the right Foundation Model (FM) for your use-case. If you prefer, you can also use Amazon SageMaker JumpStart’s ML Hub to accelerate your model development. The selected model can then be customized with additional training in AWS to suit the application’s needs. And as you program and code, the Amazon CodeWhisperer service can help as your developer coding tool.
Amazon CodeWhisperer, Al coding companion
PartyRock, an Amazon Bedrock Playground
Amazon Q, your new AI-powered assistant at work
Amazon SageMaker JumpStart ML Hub
Amazon Bedrock and Foundation Models (FMs)
Amazon Bedrock and Foundation Models (FMs)
Amazon Q, your new AI-powered assistant at work
Amazon Q, powered by Amazon Bedrock, is your workplace AI-powered assistant to answer questions. It can be connected to a variety of business software tools and aims to help employees, developers, and non-technical business users with daily tasks.
PartyRock, an Amazon Bedrock Playground
PartyRock, powered by Amazon Bedrock, is an engaging and user-friendly generative AI app-building playground. Within seconds, you can craft your unique apps, share them, and delve into the world of generative AI—all while enjoying the experience.
Amazon Bedrock and Foundation Models (FMs)
Amazon Bedrock is a fully managed service that lets you choose the FM that's best suited for your use-case. There are a broad set of FM's available including Amazon's Titan, and from leading Al startups - Al21Labs, Anthropic, co:here, Meta, Stability.Al
Amazon SageMaker JumpStart ML Hub
Amazon SageMaker JumpStart has hundreds of built-in algorithms and pre-trained models to accelerate ML model building and deployment within SageMaker, while ensuring data security.
Amazon CodeWhisperer, Al coding companion
Amazon CodeWhisperer generates code suggestions in real-time based on natural language comments, and prior code in the Integrated Development Environment (IDE). It works with many different programming languages.
Code with Amazon CodeWhisperer
Being trained on AWS data and APIs, Amazon CodeWhisperer analyzes existing code in the IDE (whether generated by CodeWhisperer or written by you), identifies problematic code with high accuracy, and provides intelligent suggestions on how to remediate it. With the customization capability more precise suggestions are generated, by including your organization’s internal APIs, libraries, classes, methods, and best practices. Explore with Amazon CodeWhisperer and get a productivity boost.
Your AI coding companion - Amazon CodeWhisperer
Build on Amazon Bedrock
With Amazon Bedrock’s comprehensive capabilities, you can experiment with a variety of Foundation Models (FMs), customize them using your data with techniques such as Fine-Tuning and Retrieval-Augmented Generation (RAG), and create managed agents. Explore now with Amazon Bedrock to execute your complex business tasks—from booking travel and processing insurance claims to creating ad campaigns and managing inventory—all without writing any code.
Amazon Titan
Amazon Titan, a family of powerful pretrained models by AWS, supports diverse use cases—deploy as is or customize privately with your data.
Developer Experience
Amazon Bedrock makes it easy for developers to work with a broad range of high-performing foundation models (FMs)
Amazon Bedrock API
Get more detailed information about the Bedrock API actions and their parameters
Integrating FMs into your code with Amazon Bedrock
Discover Amazon Bedrock and learn how to integrate gen AI models from leading Al startups and Amazon into your application
Code Examples for the AWS SDKs
Discover and execute code examples for efficient management of AWS resources, covering both single and multiple AWS services.
Train generative AI Models on Purpose-built Accelerators
Whatever customers are trying to do with FMs—running them, building them, customizing them—they need the most performant, cost-effective, purpose-built ML infrastructure. Over the past decade, AWS has been investing with our partners and silicon to offer a broad choice of high-performance, low-cost ML infrastructure chip options. The AWS Trainium and AWS Inferentia chips offer the lowest cost for generative AI training models and running inference in the cloud
AWS ML Infrastructure
Accelerate your ML innovation, AWS offers the ideal combination of high performance, cost-effective, and energy-efficient purpose-built ML tools and accelerators, optimized for ML applications.
Try Some Sample Apps
To build generative AI applications on AWS, you start with Amazon CodeWhisperer as your developer coding tool, and then use Amazon Bedrock to choose the right Foundation Model (FM) for your user-case. If you prefer, you can also use Amazon SageMaker JumpStart’s ML Hub to accelerate your model development. The selected model can then be customized with additional training in AWS to suit the application’s needs.
Build a GenAl app quickly with FMs
With Streamlit (open-source Python library), Python, Claude, Stable Diffusion, and Amazon Bedrock. Explore four distinct use cases, from image generation to text summarization, demonstrating the versatility of this new service.
GenAl in the Retail Industry
From virtual try-on experiences to crafting personalized advertisements, the impact of Stable Diffusion is far-reaching.
Maintain a Coherent Conversation with LLMs
Use LangChain with Amazon Bedrock and Amazon DynamoDB and to build applications to engage in Natural dialogue
Build Your Own Knowledge Base with Multilingual Q&A
Use Amazon Kendra, Amazon Translate, Amazon Comprehend and Amazon SageMaker JumpStart to build a multilingual knowledge base that can summarize search results.
Build Responsibly with AI
Responsible use of AI and ML is key to tackling some of humanity’s most challenging problems, augmenting human performance, and maximizing productivity. AWS is committed to developing fair and accurate AI and ML services and providing you with the tools and guidance needed to build AI and ML applications responsibly.
More Resources
Prompt Engineering for Developers
Al-Powered Development: Creating Secure and Resilient Apps
Choosing an AWS ML service
Generative AI with Large Language Models Course by DeepLearning.AI and AWS
Community
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