Categorization Metadata Responsible AI 
AWS Machine Learning Blog
MARCH 20, 2025
Built with responsible AI, Amazon Bedrock Data Automation enhances transparency with visual grounding and confidence scores, allowing outputs to be validated before integration into mission-critical workflows. Extract sentiment insights and categorize customer complaints for proactive issue resolution.
AWS Machine Learning Blog
OCTOBER 29, 2024
It’s ideal for workloads that aren’t latency sensitive, such as obtaining embeddings, entity extraction, FM-as-judge evaluations, and text categorization and summarization for business reporting tasks. It stores information such as job ID, status, creation time, and other metadata.
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
AWS Machine Learning Blog
MARCH 20, 2025
In this post, we explore why Asure used the Amazon Web Services (AWS) post-call analytics (PCA) pipeline that generated insights across call centers at scale with the advanced capabilities of generative AI-powered services such as Amazon Bedrock and Amazon Q in QuickSight. and Anthropics Claude Haiku 3.
AWS Machine Learning Blog
NOVEMBER 7, 2024
In this second part, we expand the solution and show to further accelerate innovation by centralizing common Generative AI components. We also dive deeper into access patterns, governance, responsible AI, observability, and common solution designs like Retrieval Augmented Generation. This logic sits in a hybrid search component.
AWS Machine Learning Blog
MARCH 18, 2025
SQL is one of the key languages widely used across businesses, and it requires an understanding of databases and table metadata. Today, generative AI can help bridge this knowledge gap for nontechnical users to generate SQL queries by using a text-to-SQL application. Weve added one dropdown menu with four choices.
AWS Machine Learning Blog
SEPTEMBER 13, 2024
Set up the policy documents and metadata in the data source for the knowledge base We use Amazon Bedrock Knowledge Bases to manage our documents and metadata. Upload a few insurance policy documents and metadata documents to the S3 bucket to mimic the naming conventions as shown in the following screenshot.
AWS Machine Learning Blog
NOVEMBER 14, 2024
However, model governance functions in an organization are centralized and to perform those functions, teams need access to metadata about model lifecycle activities across those accounts for validation, approval, auditing, and monitoring to manage risk and compliance. Model risk : Risk categorization of the model version.
Expert insights. Personalized for you.
106 
Let's personalize your content