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Article: Testing Machine Learning: Insight and Experience from Using Simulators to Test Trained Functionality

InfoQ Culture Methods

When testing machine learning systems, we must apply existing test processes and methods differently. Machine Learning applications consist of a few lines of code, with complex networks of weighted data points that form the implementation.

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Real-time Data, Machine Learning, and Results: The Evidence Mounts

CIO

From delightful consumer experiences to attacking fuel costs and carbon emissions in the global supply chain, real-time data and machine learning (ML) work together to power apps that change industries. more machine learning use casesacross the company. more machine learning use casesacross the company.

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Snorkel AI scores $35M Series B to automate data labeling in machine learning

TechCrunch

One of the more tedious aspects of machine learning is providing a set of labels to teach the machine learning model what it needs to know. It also announced a new tool called Application Studio that provides a way to build common machine learning applications using templates and predefined components.

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Efficient continual pre-training LLMs for financial domains

AWS Machine Learning - AI

Large language models (LLMs) are generally trained on large publicly available datasets that are domain agnostic. For example, Meta’s Llama models are trained on datasets such as CommonCrawl , C4 , Wikipedia, and ArXiv. The resulting LLM outperforms LLMs trained on non-domain-specific datasets when tested on finance-specific tasks.

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Top 10 AI graduate degree programs

CIO

The field requires broad training involving principles of computer science, cognitive psychology, and engineering. Artificial Intelligence (AI) is a fast-growing and evolving field, and data scientists with AI skills are in high demand.

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10 highest-paying IT skills for 2024

CIO

These roles include data scientist, machine learning engineer, software engineer, research scientist, full-stack developer, deep learning engineer, software architect, and field programmable gate array (FPGA) engineer.

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Why you should care about debugging machine learning models

O'Reilly Media - Data

For all the excitement about machine learning (ML), there are serious impediments to its widespread adoption. 8] Data about individuals can be decoded from ML models long after they’ve trained on that data (through what’s known as inversion or extraction attacks, for example). ML security audits.