Remove Data Quality Remove Responsible AI Remove Software Development
article thumbnail

Build a multi-tenant generative AI environment for your enterprise on AWS

AWS Machine Learning Blog

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.

article thumbnail

Reducing hallucinations in LLM agents with a verified semantic cache using Amazon Bedrock Knowledge Bases

AWS Machine Learning Blog

Previously, he was a Data & Machine Learning Engineer at AWS, where he worked closely with customers to develop enterprise-scale data infrastructure, including data lakes, analytics dashboards, and ETL pipelines. He specializes in designing, building, and optimizing large-scale data solutions.

LLM 111
professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Dr. Pandurang Kamat, Chief Technology Officer, Persistent Systems – Interview Series

Unite.AI

The bulk of Persistent Systems business comes from building software for enterprises, how has the advent of generative AI transformed how your team operates? The advent of generative AI (GenAI) has transformed how our team operates at Persistent, particularly in enterprise software development.

article thumbnail

Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning Blog

Model governance involves overseeing the development, deployment, and maintenance of ML models to help ensure that they meet business objectives and are accurate, fair, and compliant with regulations. It also helps achieve data, project, and team isolation while supporting software development lifecycle best practices.

ML 89
article thumbnail

Architect defense-in-depth security for generative AI applications using the OWASP Top 10 for LLMs

AWS Machine Learning Blog

Many customers are looking for guidance on how to manage security, privacy, and compliance as they develop generative AI applications. You can also use Amazon SageMaker Model Monitor to evaluate the quality of SageMaker ML models in production, and notify you when there is drift in data quality, model quality, and feature attribution.

article thumbnail

How Multi-Agent LLMs Can Enable AI Models to More Effectively Solve Complex Tasks

Unite.AI

Opportunities and Use Cases of LLM-MA Systems LLM-MA systems can effectively automate business processes by searching through structured and unstructured documents, generating code to query data models and performing other content generation. More LLMs and agents increase the attack surface for all AI threats.

article thumbnail

Exploring data using AI chat at Domo with Amazon Bedrock

AWS Machine Learning Blog

Generative artificial intelligence (AI) has revolutionized this by allowing users to interact with data through natural language queries, providing instant insights and visualizations without needing technical expertise. This can democratize data access and speed up analysis.