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Augmented Technologies: Flavoring Your Software with ML and AI
“New and Improved” doesn’t have the right ring any more in the world of business software. As software improves, even if incrementally, marketing forces the monikers to change. There was a period of time where ‘2.0’ was the signal for…
Artificial Intelligence: Economic Uncertainty Boosts Priority of AI and Automation Projects
COVIS-19 has convinced a majority of enterprise IT leaders that Artificial Intelligence and Machine Learning have taken on greater importance and more than half say that they plan to increase spending in those areas, according to a new report by…
Federated Machine Learning: Training ML Algorithms with Anonymous Data
Federated Learning is a way to train machine learning algorithms on data that is partitioned and distributed across many servers that each train on a subset of all data, then send their trained algorithm parameters back to a central server…
Machine Learning and SLAs: Staying on Track by Predicting Usage Trends
Machine Learning (ML) algorithms spot patterns and trends from past data. Typically, the more data that can be fed into the algorithm, the better the prediction. One example application is how ML can be used to assist IT in more…
AI and ML: Project Success Often Defined by Data Quality
Garbage in — Garbage out. Machine Learning (ML) and Artificial Intelligence (AI) algorithms often work by looking for patterns that occur across huge volumes of data, but dirty or poor data sets can throw a ringer into AI projects. Nathaniel…