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How ML System Design helps us to make better ML products

Xebia

With the industry moving towards end-to-end ML teams to enable them to implement MLOPs practices, it is paramount to look past the model and view the entire system around your machine learning model. The classic article on Hidden Technical Debt in Machine Learning Systems explains how small the model is compared to the system it operates in.

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Why GreenOps will succeed where FinOps is failing

CIO

Environmental oversight : FinOps focuses almost exclusively on financial metrics, sidelining environmental considerations, which are becoming increasingly critical for modern organizations. GreenOps incorporates financial, environmental and operational metrics, ensuring a balanced strategy that aligns with broader organizational goals.

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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?

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Navigating the cloud maze: A 5-phase approach to optimizing cloud strategies

CIO

Strategic metrics and criteria should be established to incorporate sustainability goals into various FinOps capabilities, and engineering and product teams should take responsibility for cloud usage, making appropriate choices in architecture, system design, license use and operational features.

Cloud 147
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What LinkedIn learned leveraging LLMs for its billion users

CIO

As an example, Bottaro referenced the part of the system designed to understand intent. Without automated evaluation, LinkedIn reports that “engineers are left eye-balling results and testing on a limited set of examples and having a more than a 1+ day delay to know metrics.”

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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.

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Bringing an AI Product to Market

O'Reilly Media - Ideas

In this article, we turn our attention to the process itself: how do you bring a product to market? The first step in building an AI solution is identifying the problem you want to solve, which includes defining the metrics that will demonstrate whether you’ve succeeded. Agreeing on metrics. Identifying the problem.

Marketing 145