Remove Examples Remove Metrics Remove System Design
article thumbnail

How ML System Design helps us to make better ML products

Xebia

Table of Contents What is Machine Learning System Design? Design Process Clarify requirements Frame problem as an ML task Identify data sources and their availability Model development Serve predictions Observability Iterate on your design What is Machine Learning System Design?

article thumbnail

Why GreenOps will succeed where FinOps is failing

CIO

The cost-control focus fails to engage architects and engineers in rethinking how systems are designed, built and operated for greater efficiency. Environmental oversight : FinOps focuses almost exclusively on financial metrics, sidelining environmental considerations, which are becoming increasingly critical for modern organizations.

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

What LinkedIn learned leveraging LLMs for its billion users

CIO

For example, an early version of the revised job-matching effort was rather, for the lack of a better word, rude. As an example, Bottaro referenced the part of the system designed to understand intent. Those first waves of hype around generative AI didn’t help. Or at least overly blunt.

article thumbnail

Agentic AI design: An architectural case study

CIO

An agent is part of an AI system designed to act autonomously, making decisions and taking action without direct human intervention or interaction. Some of these data points will come from the agentic AI system and some will be generated from the automation testing system. Let’s start with the basics: What is an agent?

article thumbnail

Ground truth generation and review best practices for evaluating generative AI question-answering with FMEval

AWS Machine Learning - AI

With deterministic evaluation processes such as the Factual Knowledge and QA Accuracy metrics of FMEval , ground truth generation and evaluation metric implementation are tightly coupled. For our example, we work with Anthropics Claude LLM on Amazon Bedrock.

article thumbnail

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

AWS Machine Learning - AI

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.

article thumbnail

GSAS Talk: Pragmatic Approach to Architecture Metrics – Part 1

Apiumhub

In their thought-provoking presentation titled “Pragmatic Approach to Architecture Metrics” at GSAS’22 organized by Apiumhub , Sonya Natanzon, and Vlad Khononov delivered valuable insights. Consequently, we assess the capacity of architecture to embrace change through various metrics. Whatever that is.”

Metrics 68