AWS’s GenAI Building Blocks

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AWS provides a comprehensive set of building blocks for developing, deploying, and operating Generative AI solutions at scale. From foundation models and orchestration to data integration, security, and observability, these services allow organizations to assemble GenAI architectures that are flexible, governed, and production-ready rather than opaque or experimental.

Amazon Bedrock

Offers a choice of high-performance diverse LLMs from leading AI vendors, including Anthropic, Meta, and AWS’s Nova models, with a consistent set of APIs to access and easily build applications.
Also allows for easy experimenting / switching to see which model works best for you.
Provides guardrails (security, privacy, content filtering).
Fine-tuning (private model with your data) & RAG (Retrieval Augmented Generation) – techniques to customize LLMs for your business.

Amazon SageMaker

Platform that allows the creation and deployment of LLMs and other ML models on the cloud.

Applications that AWS has built to take advantage of core FMs / LLMs

Amazon Q Business: AI-powered assistant that can handle tasks like generating content, producing summaries, etc. on enterprise data. Empowers employees to be more creative, efficient and productive. 50+ business integrations, e.g. SalesForce, Jira, etc.
Amazon Q Developer: AI-powered assistant to help developers & IT professionals with their tasks from automatic coding, testing, troubleshooting, optimizing AWS resources to documentation. Similar to MS Copilot, but Amazon Q Developer has been trained on many years of Amazon data to optimize for developing and running in an AWS environment.
Amazon Q in QuickSight: BI product that allows your analysts to build out visualizations, dashboards and perform complex calculations (Generational BI) using natural language queries.
Amazon Q in Connect: Amazon Connect is a contact center service that facilitates live interaction with customers through voice calls, live chatbots, etc. Amazon Q integration with Connect provides agents with recommendations and suggested course of action based on member identification, resulting in optimal customer service.

Other Core Building Blocks and Services:

Amazon Comprehend: Amazon Comprehend uses ML and natural language processing (NLP) to help you uncover the insights and relationships in your unstructured data. The service identifies the language of the text; extracts key phrases, places, people, brands, or events; and automatically analyzes and organizes a collection of text files by topic.
Amazon Kendra: Amazon Kendra is an intelligent search service powered by ML. Amazon Kendra reimagines enterprise search for your websites and applications so your employees and customers can easily find the content they are looking for, even when it’s scattered across multiple locations and content repositories within your organization.
Amazon Lex: Amazon Lex is a fully managed AI service to design, build, test, and deploy conversational interfaces into any application using voice and text. Lex provides the advanced deep learning functionalities of automatic speech recognition (ASR) for converting speech to text to enable you to build applications with highly engaging user experiences and lifelike conversational interactions.
Amazon Polly: Amazon Polly is an AI service that uses advanced deep learning technologies to turn text into lifelike speech. Amazon Polly lets you create applications that talk, enabling you to build entirely new categories of speech-enabled products.
Amazon Rekognition: Amazon Rekognition makes it easy to add image and video analysis to your applications using proven, highly scalable, deep learning technology that requires no ML expertise to use.
Data Protection & Application Security: AWS leverages a slew of security services like GuardDuty and Security Hub for continuous monitoring, KMS for end-to-end encryption, and various other data security and anonymization techniques aligned with standards like HIPAA, GDPR, and ISO 27001.

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