Gen AI Services on AWS: A Three-Layered Approach
Amazon Web Services (AWS) has developed a comprehensive ecosystem for generative AI, catering to different needs and expertise levels. This article explores the three main layers of AWS's approach to generative AI services.
[ 1 ] Foundation Models as a Service: Amazon Bedrock
At the top level, AWS offers Amazon Bedrock, a service that provides access to pre-trained foundation models. These large language models and other AI models can be easily integrated into applications, allowing developers to leverage powerful AI capabilities without the need for extensive AI expertise or infrastructure management.
Amazon Bedrock is ideal for scenarios where organizations need advanced AI capabilities quickly, without the hassle of data preparation, model building, or infrastructure management. It serves a wide range of use cases, including creative content generation, dialog system creation, text summarization, multilingual text creation, and advanced image generation tasks.
[ 2 ] Build Your Own Models: Amazon SageMaker and SageMaker JumpStart
For organizations looking to create custom AI models tailored to their specific needs, AWS provides Amazon SageMaker and Amazon SageMaker JumpStart. These platforms offer tools and resources for data scientists and machine learning engineers to develop, train, and deploy their own generative AI models. SageMaker provides a full suite of machine learning tools, while JumpStart offers pre-built solutions and templates to accelerate development.
SageMaker supports the complete machine learning lifecycle:
Organizations can choose from a variety of built-in algorithms and pre-trained models, or bring their own custom models. You also have control over the underlying infrastructure, such as instance types, scaling, and endpoints.
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SageMaker JumpStart is a feature that helps users to quickly get started with machine learning by providing access to a variety of pre-trained and fine-tuned models from AWS as well as some other sources like Hugging Face, Meta, AI21 Labs to name few. Users can browse, deploy, and fine-tune these models for their own use cases, or use them as a starting point for developing their own custom models.
[ 3 ] Compute: AWS Trainium and AWS Inferentia
Underpinning the AI services is AWS's specialized hardware for machine learning workloads. AWS Trainium is designed for training large AI models efficiently, while AWS Inferentia is optimized for running inference on trained models. These custom chips provide the computational power necessary for developing and deploying generative AI at scale.
By offering these three layers of services, AWS aims to democratize access to generative AI technology. Whether an organization wants to use pre-trained models, develop custom solutions, or optimize their AI infrastructure, AWS provides the tools and services to support generative AI initiatives across various stages of complexity and customization.
This layered approach allows businesses and developers to choose the level of involvement that best suits their needs, from turnkey solutions to fully customized AI development environments, all while leveraging the scalability and reliability of AWS's cloud infrastructure.
Technical Comparison : AWS Bedrock vs AWS SageMaker
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