In the era of big data and machine learning, organizations worldwide are racing to leverage the power of data to drive innovation, enhance decision-making and gain a competitive edge. However, amidst the excitement surrounding the potential of machine learning models, there lurks a hidden threat: data leaks. TechXplore brought this topic to us in their article, “Data leaks can sink machine learning models.“
Data leaks, also known as data breaches or data exposures, occur when sensitive or confidential information is inadvertently exposed to unauthorized parties. These leaks can occur due to a variety of reasons, including misconfigured databases, phishing attacks or insider threats. Data leaks can involve personal information, financial data or any other type of sensitive data, posing significant risks to individuals’ privacy and organizations’ security.
Data is the lifeblood of machine learning models. These models rely on large volumes of high-quality data to learn patterns, make predictions and generate insights. However, when data leaks occur, they can compromise the integrity, reliability and performance of machine learning models in many ways.
At the end of the day, data needs to be findable, and that happens with a strong, standards-based taxonomy. Data Harmony is our patented, award winning, artificial intelligence (AI) suite that leverages explainable AI for efficient, innovative and precise semantic discovery of your new and emerging concepts, to help you find the information you need when you need it.
Melody K. Smith
| Data Harmony is an award-winning semantic suite that leverages explainable AI. |
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Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.
