Category: Generative AI

Amazon Q and generative BI are transforming business operations, and AWS Partners like ZS Associates, Tiger Analytics, and Compass UOL are pioneering use cases leveraging these technologies to build industry-tailored solutions that improve decision making, operations, fraud detection, software development lifecycles, and more. AWS provides resources to help partners develop and deploy such transformative generative AI offerings.

Generative AI adoption is rapidly growing to meet the massive data needs of modern machine learning models. Manually labeling data can be time-consuming but AWS has collaborated with Datasaur to offer solutions addressing data labeling challenges using generative AI. Datasaur’s NLP Platform automates annotation tasks and integrates with AWS services, and its LLM Labs evaluates large language models’ performance and cost for labeling.

Fine-tuning large language models (LLMs) is crucial for leveraging their full potential across industries. Quantiphi unveils how fine-tuning supercharges LLMs to deliver domain-specific AI solutions that redefine possibilities. From personalized healthcare to precise financial predictions and streamlined legal reviews, fine-tuned models offer transformative value and unleash the power of customized, efficient, and responsible generative AI deployments.

Pinecone has developed a novel serverless vector database architecture optimized for AI workloads like retrieval-augmented generation. Built on AWS, it decouples storage and compute and enables efficient intermittent querying of large datasets. This provides elasticity, fresher data, and major cost savings over traditional architectures. Pinecone serverless removes bottlenecks to building more knowledgeable AI applications economically at scale on AWS.

Contact centers can improve customer experiences using generative AI, which creates new content and conversations. Accenture’s Connected Customer Experience (CCE) solution incorporates AWS services to provide personalized human and AI interactions. It uses generative AI for agent assist, call summarization, and self-service FAQs. By leveraging generative AI on AWS, CCE aims to enhance agent productivity, reduce handle times, and deliver exceptional customer experiences.

The Hugging Face Platform provides no-code and low-code solutions for deploying generative AI models on managed AWS infrastructure. Key features include Inference Endpoints for easy model deployment, Spaces for hosting machine learning apps, and AutoTrain for training state-of-the-art models without coding. Hugging Face is an AWS Generative AI Competency Partner whose mission is to democratize machine learning through open source, open science, and Hugging Face products and services.

AWS embraces the “working backwards” approach to stay customer-focused. The Generative AI Center of Excellence (CoE) for AWS Partners applies this methodology and collects partner feedback to provide relevant insights, tools, and resources on leveraging generative AI. Recent updates to the CoE include customer research on generative AI adoption challenges, a usage maturity heatmap by industry, and five new use case deep dives covering telecom, automotive, IDP, contact centers, and financial analysts.

With the ability to automate tasks, enhance productivity, and enable hyper-personalized customer experiences, businesses are seeking specialized expertise to build a successful generative AI strategy. To support this need, we’re excited to announce the AWS Generative AI Competency—an AWS Specialization that helps AWS customers more quickly adopt generative AI solutions and strategically position themselves for the future.

Marriott’s CISO Arno Van Der Walt manages cybersecurity through a “human-centered, data-driven, technology-enabled” approach aimed at making security frictionless. Critical partnerships with AWS, Deloitte, and Palo Alto Networks leverage AI/ML to share threat data and empower “impossible” autonomous security. Together, their tri-party services provide an end-to-end platform unifying business and security data to detect threats and enable quick response.

Large language models like Amazon Bedrock can generate MongoDB queries from natural language questions, transforming how users access NoSQL databases. By leveraging AI and language models, this solution allows business users to query MongoDB data through conversational English instead of code. It connects to MongoDB with PyMongo, generates queries with LangChain and Bedrock, retrieves and formats results into natural language answers.