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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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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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Testing the Question Answering Capabilities of Large Language Models

John Snow Labs

Furthermore, we’ll perform robustness testing for Large Language Models and evaluate them using various evaluation metrics, including Embedding Distance Metrics, String Distance Metrics, and QAEvalChain approach inspired by the LangChain library. Consider a QA system designed to provide medical advice.

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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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Empathetic Technology: The Future of Workplace DE&I?

Hacker Earth Developers Blog

This kind of tool allows you to store all your information in a secure database that links up your systems. ERP tools thus level the playing field, which is exactly what you need if you want to focus on DEI. This term covers the use of any tech-based tools or systems designed to understand and respond to human emotions.

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Simulation Theory, Observability, and Modern Software Practices

Honeycomb

s favorite three buzzwords (logs, metrics, and traces), we can draw several analogies to understand software development and debugging. The real vs. simulated systems In Baudrillard’s terms, the authentic experiences and the real have been replaced by symbols and signs ( logs , metrics , traces ).

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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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