Today, we’re adding Automated Reasoning checks (preview) as a new safeguard in Amazon Bedrock Guardrails to help you mathematically validate the accuracy of responses generated by large language models (LLMs) and prevent factual errors from hallucinations. Amazon Bedrock Guardrails lets you implement safeguards for generative AI applications by filtering undesirable content, redacting personal 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 now Automated Reasoning checks. Automated Reasoning checks help prevent factual errors from hallucinations using sound mathematical, logic-based algorithmic verification and reasoning processes to verify the information generated by a model, so outputs align with known facts and aren’t based on fabricated…
Tag: AWS News Blog
Build faster, more cost-efficient, highly accurate models with Amazon Bedrock Model Distillation (preview)
Today, we’re announcing the availability of Amazon Bedrock Model Distillation in preview that automates the process of creating a distilled model for your specific use case by generating responses from a large foundation model (FM) called a teacher model and fine-tunes a smaller FM called a student model with the generated responses. It uses data synthesis techniques to improve response from a teacher model. Amazon Bedrock then hosts the final distilled model for inference giving you a faster and more cost-efficient model with accuracy close to the teacher model, for your use case. Customers are excited to use the most powerful and accurate FMs on Amazon Bedrock for their generative AI applications. But for some use cases, the latency associated…
Introducing queryable object metadata for Amazon S3 buckets (preview)
AWS customers make use of Amazon Simple Storage Service (Amazon S3) at an incredible scale, regularly creating individual buckets that contain billions or trillions of objects! At that scale, finding the objects which meet particular criteria — objects with keys that match a pattern, objects of a particular size, or objects with a specific tag — becomes challenging. Our customers have had to build systems that capture, store, and query for this information. These systems can become complex and hard to scale, and can fall out of sync with the actual state of the bucket and the objects within. Rich Metadata Today we are enabling in preview automatic generation of metadata that is captured when S3 objects are added or…
New Amazon S3 Tables: Storage optimized for analytics workloads
Amazon S3 Tables give you storage that is optimized for tabular data such as daily purchase transactions, streaming sensor data, and ad impressions in Apache Iceberg format, for easy queries using popular query engines like Amazon Athena, Amazon EMR, and Apache Spark. When compared to self-managed table storage, you can expect up to 3x faster query performance and up to 10x more transactions per second, along with the operational efficiency that is part-and-parcel when you use a fully managed service. Iceberg has become the most popular way to manage Parquet files, with thousands of AWS customers using Iceberg to query across often billions of files containing petabytes or even exabytes of data. Table Buckets, Tables, and Namespaces Table buckets are…