AWS Generative AI: The Basics
AWS is at the forefront of the rapidly evolving generative AI landscape with its groundbreaking tools.
While we can only speculate about the ongoing changes for Bedrock, it's confirmed that these models will be accessible in SageMaker JumpStart.
GenAI's potential extends across departments, empowering customer service teams with chatbots powered by machine learning (ML) for natural interactions, enabling developers to accelerate coding, and assisting sales and marketing teams with powerful message templates.
Discover the latest AWS AI offerings and how genAI tools unlock new creative expression, enhance customer experiences, and drive business growth.
Leveraging GenAI on AWS
AWS has a deep history with genAI. Tools like Textract, Transcribe and Comprehend create the building blocks needed to create the datasets to train models. QuickSight Q, which launched in 2021, lets users ask business questions using natural language and receive accurate answers and relevant visualizations.
At the core of genAI is the concept of foundation models (FMs). These types of ML models are pre-trained on diverse datasets and many forms of content, including books, articles, websites, social media and conversational data.
By learning from this trove of information, FMs develop a deeper understanding of the underlying data distribution. From there, you can generate new content that aligns with the patterns and characteristics of the training data. These ML models power algorithms, which use this learned knowledge to generate content autonomously.
Whether you’re using AI for generating images, coherent text, music or product designs, the right FMs help you realize greater creative potential, efficiency and performance. Recently, Amazon introduced its Amazon Bedrock service and Titan FMs, which consist of two large language models (LLMs).
As of August 2023, Amazon Bedrock isn't publicly available, but here is what you can expect to see.
Bedrock makes it easy for companies to build genAI applications by providing access to Amazon’s Titan FMs, along with FMs from AI21 Labs, Anthropic and Stability AI via an API. Bedrock makes these FMs available to all builders and empowers them to create and scale generative AI-based applications.
Bedrock’s serverless experience helps you quickly and easily get started with FMs. They can be customized, integrated and deployed into applications using AWS tools and capabilities users already know well, including Amazon SageMaker features such as Experiments and Pipelines.
Through Bedrock's scalable and secure AWS-managed service, customers can access the Jurassic-2 family of multilingual language models from AI21 Labs, which excel in following natural language instructions, allowing users to generate text in several languages, including Spanish, French and German.
Other notable offerings within Bedrock include Anthropic's Claude, an LLM known for its conversational and text-processing capabilities. Claude assists with tasks including summarization, rewriting, and generating questions and answers based on a variety of text-based content. Anthropic’s Constitutional AI training is designed to ensure its functionality causes no harm. Additionally, the company has announced plans for the Claude 2 model.
Stability AI’s text-to-image FMs are also provided. The company’s models include the popular Stable Diffusion, which offers advanced photorealism and imaging capabilities.
Bedrock makes customization easy. You can fine-tune models for specific tasks by providing a few labeled examples from your Amazon S3 storage. Based on that input, Bedrock will train a private copy of the model while safeguarding data privacy and confidentiality. With as few as 20 examples, you can generate valuable tailored content that aligns with your distinct business requirements.
For example, an e-commerce business could use Bedrock and Stability AI's text-to-image FMs to improve product listings and promotional material. After your business provides labeled examples of product descriptions and corresponding images, Bedrock can generate high-quality and visually appealing images that accurately represent those products. This enables you to improve the look and feel of your product listings, making for a more attractive e-commerce platform that keeps consumers’ attention and generates conversions.
AWS also offers Amazon Titan, which was introduced with two LLMs. One of these is Titan Text, a generative LLM capable of tasks including summarization, text generation, and information extraction. This versatile model enables you to automate labor-intensive tasks and realize new insights from existing information.
Text-related processes, such as summarizing lengthy documents, generating coherent and contextually relevant text, and extracting insights from large datasets, can be streamlined with Titan Text. For example, a service provider could use Titan Text to automatically create customer quotes and other time-consuming documentation.
The second model within Titan is Titan Embeddings, which focuses on translating text inputs to numerical representations. These are known as embeddings, and they contain the semantic meaning of the text in numerical form. This feature helps deliver relevant and contextual responses for functions such as personalization and search. By comparing embeddings, you can improve the user experience and the accuracy of content recommendations.
AWS’ commitment to responsible AI usage extends to its Titan LLMs, which detect and remove harmful content from data, reject inappropriate user inputs, and filter outputs based on content.
Expanded EC2 Instances
Amazon also announced the general availability of two new instances, Amazon EC2 Trn1n and Amazon EC2 Inf2, which are powered by AWS Trainium and AWS Inferentia2, respectively. These instances provide cost-effective cloud infrastructure for genAI applications.
Trn1n instances can offer up to 50% savings on training costs compared with other EC2 instances. These instances are designed to distribute training across multiple servers and come with high-speed networking capabilities. They can be deployed in UltraClusters with tens of thousands of Trainium chips, adding scalability to ML workloads. Trn1n instances facilitate deep learning training while maintaining performance.
Meanwhile, Inf2 instances are optimized for large-scale genAI applications with models containing up to hundreds of billions of parameters. These instances deliver higher throughput and lower latency than previous generation instances, resulting in up to 40% better inference price performance. Inf2 instances are specifically designed for deep learning inference.
Amazon has expanded the availability of Amazon CodeWhisperer, an AI coding companion. CodeWhisperer uses genAI to improve developer productivity by generating code suggestions in real time based on natural language comments and prior code in the developer's integrated development environment (IDE).
The tool supports multiple programming languages and can be integrated into popular IDEs, such as VS Code and IntelliJ IDEA. CodeWhisperer has undergone extensive training on publicly available code and Amazon's own codebase, making it accurate, fast and secure. The service also includes security scanning capabilities to detect and suggest remediations for vulnerabilities. Responsible code generation is ensured, as CodeWhisperer can filter biased or unfair suggestions. This service is free to individual users, while a professional tier with additional features is offered for business users.
Maximizing the Benefits of Amazon’s GenAI Services
From high-performing FMs carefully crafted by leading experts to seamless integration with your existing workflows, AWS offers cutting-edge capabilities without the burden of developing your own solutions. Customize generative AI models to suit your business needs, ensuring data security and privacy, and deliver personalized solutions to your clients. Explore the possibilities of Amazon's generative AI services and harness the potential of AI for your organization.
One of the key challenges companies face when leveraging genAI is finding and accessing high-performing FMs that can deliver outstanding results while meeting user expectations. Amazon's genAI offerings make it easy for you to identify and apply the most suitable FMs for your company’s applications, including custom requirements.
Bedrock provides a curated selection of state-of-the-art FMs, carefully designed and trained by leading institutions and AI experts. These FMs have undergone rigorous testing and evaluation to ensure exceptional performance across tasks.
Amazon’s genAI services provide businesses like yours with cutting-edge capabilities and save you from developing your own solutions or finding other third parties.
Hosting LLMs Managed by AWS
Integrating AI capabilities into applications can require significant infrastructure investment. Your costs can quickly add up because of complex cloud setups and the need for extensive computing resources. However, with managed LLMs on AWS, you no longer have to set up and maintain your own infrastructure. The integration process is designed to be straightforward and hassle-free.
Amazon's genAI services seamlessly integrate with your existing applications and workflows, allowing you to immediately leverage the power of AI. By streamlining your workflows, AWS lets you focus on innovation without technical barriers.
Customized and Secure Applications
Amazon recognizes the value of business data and the need to customize generative AI models without jeopardizing data security or privacy. With AWS’ genAI services, you can effortlessly build differentiated applications regardless of dataset size.
For example, a financial services company is trying to provide personalized investment recommendations to its clients. By leveraging client data, including financial profiles, investment preferences and risk tolerance, the company can create a private copy of an FM using Amazon's genAI services.
Using examples from that dataset, the financial services company can fine-tune the genAI model to generate tailored investment strategies that align with each client's unique financial goals and risk appetite. The model can account for factors such as income level, investment horizon and desired asset allocation.
This customization process enables the company to deliver personalized investment recommendations that optimize returns while considering each client's individual circumstances. Throughout the process, Amazon's generative AI services prioritize data protection, maintaining confidentiality and privacy. The financial services company retains full control over how its data is shared and used, creating peace of mind while safeguarding intellectual property and meeting compliance requirements.
Finding Experienced Support for AWS GenAI
AWS’ generative AI tools give businesses a suite of solutions to improve business performance and results without the headaches and risk of building out their own software from scratch. Amazon Bedrock acts as a gateway to leading models including Claude, Stable Diffusion and Amazon Titan for a variety of use cases. With Bedrock, you can scale genAI applications seamlessly without sacrificing customization or security.
As the demand for sophisticated models grows, businesses seek vertical-specific solutions tailored to their needs, creating new opportunities for genAI to foster innovation and growth.
However, the key challenge lies in finding the right experts who understand the technology and your business goals. Effectively harnessing genAI applications demands technical knowledge and experience.
Collaborate with an experienced AWS Premier Tier Services provider like Mission Cloud to navigate these complexities. We provide deep expertise in AWS technology, AI implementation, and genAI applications.
Learn how a partnership with Mission Cloud can help you reach your genAI goals while mitigating risks and maximizing ROI.
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