Amazon Web Services

Amazon Q Business is a generative AI–powered assistant that can answer questions, provide summaries, generate content, and complete tasks based on data and information in your enterprise systems.

Amazon Q Apps empowers every employee to quickly turn their ideas into generative AI–powered apps to streamline tasks and boost individual and team productivity.

Join Swami Sivasubramanian, senior vice president of AI and ML at AWS, as he talks to Praerit Garg, President of Product & Innovation at Smartsheet, about how Smartsheet is using generative AI to

See how Generative BI capabilities of Amazon QuickSight can improve productivity and accelerate data-driven decision making.

Integrating data across multiple sources can take days or even months.

Security leaders face complex, high-stakes challenges every day.

Neeraj Chauhan, CIO PayU India, talks about the close collaboration with AWS Enterprise Support to manage their operations in a cost-effective, secure, and efficient manner that can scale.

Nissan harnesses the power of the cloud to make more data-driven decisions across their global organization.

This video walks through an overview of AWS Marketplace professional services, including various categories, partner benefits, and features.

Monitoring Amazon DynamoDB Tables presented by Abhimanyu Chhabra, Lead Consultant - Databases AWS In this video, we explore the key Amazon CloudWatch Monitoring Metrics for Amazon DynamoDB.

Pinterest provides inspiration for millions of Pinners to create a life they love.

If you have already adopted graph, it is important to take an inventory of the actual schema of your graph. Ideally data that lands in a database adheres to your intended data model.

Graph modeling is hard, but it's crucial to the success of a project to build a good stage one graph model quickly to keep momentum.

Continuously validating software and bringing new functionality to customers faster requires vast amounts of data.

Learn how to use open-source examples to easily build your own FM evaluation pipeline on Amazon SageMaker to evaluate and deploy FMs at scale on AWS.

You can customize your own prompt datasets and scoring algorithms specific for your generative AI application. Learn how to build and prepare your own datasets for FM evaluation on Amazon SageMaker.

Some evaluation criteria are nuanced or subjective and require human judgement to assess.

Amazon SageMaker Clarify automatic FM evaluation is available as an open-source library in GitHub to allow you to run it anywhere.

Amazon SageMaker Clarify now supports foundation model (FM) evaluation to help you quickly evaluate, compare, and select the best FM for your use case based on a variety of criteria across different

Join Avichal as he explores essential security patterns and best practices for hardening your Amazon Elastic Kubernetes Service (EKS) clusters on AWS.

00:00 - 01:50 - Introduction 01:50 - 03:20 - What is Amazon Neptune? 03:20 - 04:34 - What is Amazon Neptune Analytics? 04:34 - 06:47 - How does graph databases play a role in generative AI?

Hear Rickard Wieselfors, VP and Head of Automation & AI, discuss how Ericsson accelerated their innovation journey by migrating their SAP systems to AWS, lowering infrastructure costs, increasing

Security is job zero, and follows a shared responsibility model at AWS.

Guardrails for Amazon Bedrock is the only solution offered by a top cloud provider that enables customers to build and customize safety and privacy protections for their generative AI applications

New Model Evaluation simplifies the process of accessing, comparing, and selecting LLMs and FMs - Choosing the appropriate model is a critical first step toward building any generative AI application.

Agents for Amazon Bedrock plan and execute multistep tasks using company systems and data sources—from answering customer questions about your product availability to taking their orders.

With new Custom Model Import for Amazon Bedrock, customers can leverage the full capabilities of Amazon Bedrock with their proprietary models.

All Amazon Titan foundation models are designed with ethical AI principles in mind, incorporating measures to detect and remove harmful content, reject inappropriate inputs, and filter model outputs.

All Amazon Titan foundation models are built with responsible AI in mind by detecting and removing harmful content from the data, rejecting inappropriate user inputs, and filtering model outputs.

Guardrails for Amazon Bedrock enables customers to implement safeguards across large language models (LLMs) based on their use cases and responsible AI policies.