AWS Machine Learning Blog
Tag: Generative AI
Customize small language models on AWS with automotive terminology
In this post, we guide you through the phases of customizing SLMs on AWS, with a specific focus on automotive terminology for diagnostics as a Q&A task. We begin with the data analysis phase and progress through the end-to-end process, covering fine-tuning, deployment, and evaluation. We compare a customized SLM with a general purpose LLM, using various metrics to assess vocabulary richness and overall accuracy.
Automate cloud security vulnerability assessment and alerting using Amazon Bedrock
This post demonstrates a proactive approach for security vulnerability assessment of your accounts and workloads, using Amazon GuardDuty, Amazon Bedrock, and other AWS serverless technologies. This approach aims to identify potential vulnerabilities proactively and provide your users with timely alerts and recommendations, avoiding reactive escalations and other damages.
How MSD uses Amazon Bedrock to translate natural language into SQL for complex healthcare databases
MSD, a leading pharmaceutical company, collaborates with AWS to implement a powerful text-to-SQL generative AI solution using Amazon Bedrock and Anthropic’s Claude 3.5 Sonnet model. This approach streamlines data extraction from complex healthcare databases like DE-SynPUF, enabling analysts to generate SQL queries from natural language questions. The solution addresses challenges such as coded columns, non-intuitive names, and ambiguous queries, significantly reducing query time and democratizing data access.
Principal Financial Group uses QnABot on AWS and Amazon Q Business to enhance workforce productivity with generative AI
In this post, we explore how Principal used QnABot paired with Amazon Q Business and Amazon Bedrock to create Principal AI Generative Experience: a user-friendly, secure internal chatbot for faster access to information. Using generative AI, Principal’s employees can now focus on deeper human judgment based decisioning, instead of spending time scouring for answers from data sources manually.
Revolutionize trip planning with Amazon Bedrock and Amazon Location Service
In this post, we show you how to build a generative AI-powered trip-planning service that revolutionizes the way travelers discover and explore destinations. By using advanced AI technology and Amazon Location Service, the trip planner lets users translate inspiration into personalized travel itineraries. This innovative service goes beyond traditional trip planning methods, offering real-time interaction through a chat-based interface and maintaining scalability, reliability, and data security through AWS native services.
Transcribe, translate, and summarize live streams in your browser with AWS AI and generative AI services
In this post, we explore the approach behind building an AWS AI-powered Chrome extension that aims to revolutionize the live streaming experience by providing real-time transcription, translation, and summarization capabilities directly within your browser.
Build a reverse image search engine with Amazon Titan Multimodal Embeddings in Amazon Bedrock and AWS managed services
In this post, you will learn how to extract key objects from image queries using Amazon Rekognition and build a reverse image search engine using Amazon Titan Multimodal Embeddings from Amazon Bedrock in combination with Amazon OpenSearch Serverless Service.
Fine-tune Meta Llama 3.2 text generation models for generative AI inference using Amazon SageMaker JumpStart
In this post, we demonstrate how to fine-tune Meta’s latest Llama 3.2 text generation models, Llama 3.2 1B and 3B, using Amazon SageMaker JumpStart for domain-specific applications. By using the pre-built solutions available in SageMaker JumpStart and the customizable Meta Llama 3.2 models, you can unlock the models’ enhanced reasoning, code generation, and instruction-following capabilities to tailor them for your unique use cases.
Integrate foundation models into your code with Amazon Bedrock
The rise of large language models (LLMs) and foundation models (FMs) has revolutionized the field of natural language processing (NLP) and artificial intelligence (AI). These powerful models, trained on vast amounts of data, can generate human-like text, answer questions, and even engage in creative writing tasks. However, training and deploying such models from scratch is […]
Unlock organizational wisdom using voice-driven knowledge capture with Amazon Transcribe and Amazon Bedrock
This post introduces an innovative voice-based application workflow that harnesses the power of Amazon Bedrock, Amazon Transcribe, and React to systematically capture and document institutional knowledge through voice recordings from experienced staff members. Our solution uses Amazon Transcribe for real-time speech-to-text conversion, enabling accurate and immediate documentation of spoken knowledge. We then use generative AI, powered by Amazon Bedrock, to analyze and summarize the transcribed content, extracting key insights and generating comprehensive documentation.