Remove Analytics Remove Government Remove Metrics
article thumbnail

Best practices for Amazon SageMaker HyperPod task governance

AWS Machine Learning

Administrators can use SageMaker HyperPod task governance to govern allocation of accelerated compute to teams and projects, and enforce policies that determine the priorities across different types of tasks. We also discuss common governance scenarios when administering and running generative AI development tasks.

article thumbnail

Principal Financial Group uses QnABot on AWS and Amazon Q Business to enhance workforce productivity with generative AI

AWS Machine Learning

Principal implemented several measures to improve the security, governance, and performance of its conversational AI platform. Additional integrations with services like Amazon Data Firehose , AWS Glue , and Amazon Athena allowed for historical reporting, user activity analytics, and sentiment trends over time through Amazon QuickSight.

Chatbots 114
Insiders

Sign Up for our Newsletter

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

Trending Sources

article thumbnail

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

AWS Machine Learning

We also dive deeper into access patterns, governance, responsible AI, observability, and common solution designs like Retrieval Augmented Generation. Model monitoring – The model monitoring service allows tenants to evaluate model performance against predefined metrics.

article thumbnail

Unlocking insights and enhancing customer service: Intact’s transformative AI journey with AWS

AWS Machine Learning

In this post, we demonstrate how the CQ solution used Amazon Transcribe and other AWS services to improve critical KPIs with AI-powered contact center call auditing and analytics. Additionally, Intact was impressed that Amazon Transcribe could adapt to various post-call analytics use cases across their organization.

article thumbnail

Unlock the power of data governance and no-code machine learning with Amazon SageMaker Canvas and Amazon DataZone

AWS Machine Learning

Amazon DataZone is a data management service that makes it quick and convenient to catalog, discover, share, and govern data stored in AWS, on-premises, and third-party sources. However, ML governance plays a key role to make sure the data used in these models is accurate, secure, and reliable. For Select a data source , choose Athena.

article thumbnail

How to Successfully Implement Customer Journey Analytics – Part 1

Pointillist

Companies are increasingly benefiting from customer journey analytics across marketing and customer experience, as the results are real, immediate and have a lasting effect. Learning how to choose the best customer journey analytics platform is just the start. Steps to Implement Customer Journey Analytics. By Swati Sahai.

article thumbnail

Track LLM model evaluation using Amazon SageMaker managed MLflow and FMEval

AWS Machine Learning

SageMaker is a data, analytics, and AI/ML platform, which we will use in conjunction with FMEval to streamline the evaluation process. Evaluation algorithm Computes evaluation metrics to model outputs. Different algorithms have different metrics to be specified. We specifically focus on SageMaker with MLflow.