Under on-premise machine learning platforms, algorithms are trained to find patterns and features in mass amounts of data. Using this data, the algorithms make crucial decisions and predictions. The better the algorithm, the more accurate its decisions and predictions will become.
Today, examples of on-premise machine learning are all around us. Digital assistants search the web and play music in response to our voice commands. Websites recommend products and movies and songs based on what we bought, watched, or listened to before. Robots vacuum our floors while we do something better with our time. The future of machine learning for on-premise platforms is just beginning.
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