Machine Learning and Databases

April 7, 2022 / Melody Smith / Comments Off on Machine Learning and Databases

Data sets for machine learning solve big data problems, from data collection and storage through analysis and the training of machine learning models to the deployment of real-time prediction endpoints. VentureBeat brought this interesting information to our attention in their article, “Database technology evolves to combine machine learning and data storage.”

This new approach offers not just the potential for tapping the power of artificial intelligence (AI) algorithms, but also a more flexible search engine that isn’t locked into searching for exact matches.

Most organizations have little visibility and knowledge of how AI systems make the decisions they do and how the results are being applied in the various fields. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. “Explainable AI” is used to describe an AI model, its expected impact and potential biases. Explainability becomes critical when the results can have an impact on data security or safety.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, changing search to found.

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Melody Smith

Melody K. Smith has provided organizational, social media and digital communication services to a large non-profit for the past twenty years. Prior to that she championed employee engagement and communications in the healthcare field. She holds a Bachelors degree in Marketing. When not wrangling and writing TaxoDiary content for your reading pleasure, Melody writes fiction, rescues dogs and throws legendary dinner parties.