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Why GreenOps will succeed where FinOps is failing

CIO

Architects must combine functional requirements with multiple other long-term requirements to build sustainable systems. Overemphasis on tools, budgets and controls. The cost-control focus fails to engage architects and engineers in rethinking how systems are designed, built and operated for greater efficiency.

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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?

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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. Especially with companies like Microsoft, OpenAI, Meta, Salesforce and others in the news recently with announcements of agentic AI and agent creation tools and capabilities.

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Bringing an AI Product to Market

O'Reilly Media - Ideas

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. It sounds simplistic to state that AI product managers should develop and ship products that improve metrics the business cares about. Agreeing on metrics.

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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. System/Platform Specification - Name specific systems, platforms, or tools - Example: "What steps are required in [System Name] to [specific action]?"

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The Importance of Evidence-Based Hiring in Tech: A Complete Guide

Hacker Earth Developers Blog

Enter evidence-based hiring , a data-driven approach that focuses on measurable metrics, validated assessments, and analytics to identify the right talent. Evidence-based hiring uses objective data, validated tools, and analytics to evaluate candidates skills, behaviors, and potential. What is evidence-based hiring?

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Cybersecurity Snapshot: Insights on Hive Ransomware, Supply Chain Security, Risk Metrics, Cloud Security

Tenable

Get the latest on the Hive RaaS threat; the importance of metrics and risk analysis; cloud security’s top threats; supply chain security advice for software buyers; and more! . Defending against Hive ransomware: It’s time to use the attackers’ tools ” (The Stack). Researcher develops Hive ransomware decryption tool ” (TechTarget).

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