Today, we announced a new capability of Amazon Q in QuickSight that helps users perform scenario analyses to find answers to complex problems quickly. This AI-assisted data analysis experience helps business users find answers to complex problems by guiding them step-by-step through in-depth data analysis—suggesting analytical approaches, automatically analyzing data, and summarizing findings with suggested actions—using natural language prompts. This new capability eliminates hours of tedious and error-prone manual work traditionally required to perform analyses using spreadsheets or other alternatives. In fact, Amazon Q in QuickSight enables business users to perform complex scenario analysis up to 10x faster than spreadsheets. This capability expands upon existing data Q&A capabilities of Amazon QuickSight so business professionals can start their analysis by simply…
Category: AWS
Reposts from Amazon Web Services (AWS).
Use Amazon Q Developer to build ML models in Amazon SageMaker Canvas
As a data scientist, I’ve experienced firsthand the challenges of making machine learning (ML) accessible to business analysts, marketing analysts, data analysts, and data engineers who are experts in their domains without ML experience. That’s why I’m particularly excited about today’s Amazon Web Services (AWS) announcement that Amazon Q Developer is now available in Amazon SageMaker Canvas. What catches my attention is how Amazon Q Developer helps connect ML expertise with business needs, making ML more accessible across organizations. Amazon Q Developer helps domain experts build accurate, production-quality ML models through natural language interactions, even if they don’t have ML expertise. Amazon Q Developer guides these users by breaking down their business problems and analyzing their data to recommend step-by-step…
Amazon Bedrock Guardrails now supports multimodal toxicity detection with image support (preview)
Today, we’re announcing the preview of multimodal toxicity detection with image support in Amazon Bedrock Guardrails. This new capability detects and filters out undesirable image content in addition to text, helping you improve user experiences and manage model outputs in your generative AI applications. Amazon Bedrock Guardrails helps you implement safeguards for generative AI applications by filtering undesirable content, redacting personally identifiable information (PII), and enhancing content safety and privacy. You can configure policies for denied topics, content filters, word filters, PII redaction, contextual grounding checks, and Automated Reasoning checks (preview), to tailor safeguards to your specific use cases and responsible AI policies. With this launch, you can now use the existing content filter policy in Amazon Bedrock Guardrails to…
New Amazon Bedrock capabilities enhance data processing and retrieval
Today, Amazon Bedrock introduces four enhancements that streamline how you can analyze data with generative AI: Amazon Bedrock Data Automation (preview) – A fully managed capability of Amazon Bedrock that streamlines the generation of valuable insights from unstructured, multimodal content such as documents, images, audio, and videos. With Amazon Bedrock Data Automation, you can build automated intelligent document processing (IDP), media analysis, and Retrieval-Augmented Generation (RAG) workflows quickly and cost-effectively. Insights include video summaries of key moments, detection of inappropriate image content, automated analysis of complex documents, and much more. You can customize outputs to tailor insights into your specific business needs. Amazon Bedrock Data Automation can be used as a standalone feature or as a parser when setting up…