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Ground truth curation and metric interpretation best practices for evaluating generative AI question answering using FMEval

AWS Machine Learning

This post focuses on evaluating and interpreting metrics using FMEval for question answering in a generative AI application. FMEval is a comprehensive evaluation suite from Amazon SageMaker Clarify , providing standardized implementations of metrics to assess quality and responsibility. Question Answer Fact Who is Andrew R.

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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

The DS uses SageMaker Training jobs to generate metrics captured by , selects a candidate model, and registers the model version inside the shared model group in their local model registry. You can use the method mlflow.autolog() to log metrics, parameters, and models without the need for explicit log statements.

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Why Technology Won’t Help You Understand Your Customers

C3Centricity

A 2015 Capgemini and EMC study called “Big & Fast Data: The rise of Insight-Driven Business” showed that: 56% of the 1,000 senior decision makers surveyed claim that their investment in big data over the next three years will exceed past investment in information management. ” Bold words indeed!

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How Amp on Amazon used data to increase customer engagement, Part 2: Building a personalized show recommendation platform using Amazon SageMaker

AWS Machine Learning

This is Part 2 of a series on using data analytics and ML for Amp and creating a personalized show recommendation list platform. The platform has shown a 3% boost to customer engagement metrics tracked (liking a show, following a creator, enabling upcoming show notifications) since its launch in May 2022.

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You’ve Got Data? Well Don’t Start There!

C3Centricity

A 2015 Capgemini and EMC study called “Big & Fast Data: The rise of Insight-Driven Business” showed that: 56% of the 1,000 senior decision makers surveyed claim that their investment in big data over the next three years will exceed past investment in information management. ” Bold words indeed!

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How Amp on Amazon used data to increase customer engagement, Part 1: Building a data analytics platform

AWS Machine Learning

Amp wanted a scalable data and analytics platform to enable easy access to data and perform machine leaning (ML) experiments for live audio transcription, content moderation, feature engineering, and a personal show recommendation service, and to inspect or measure business KPIs and metrics. Data Engineer for Amp on Amazon.

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Why Do You Need to Rethink Your Customer’s Journey

ProProfs Blog

You’ll even find a dozen people on YouTube who give you valuable advice about these cars while entertaining you. This is why Google names Big Data and machine learning as the next steps for understanding customers. Check these Help Desk Metrics. No wonder 88% of customers research properly before making a purchase.