Here’s how it works: Machine learning algorithms consume and process large volumes of data to learn complex patterns about people, business processes, transactions, events, and so on. This intelligence is then incorporated into a predictive model. Comparisons to the model can reveal whether an entity is operating within acceptable parameters or is an anomaly.
Machine learning is being used today to solve well-bounded tasks such as classification and clustering. Note that a machine learning algorithm learns from so-called training data during development; it also learns continuously from real-world data during deployment so the algorithm can improve its model with experience.
This report will drill into the data, tool, and platform requirements for machine learning with a focus on automating and optimizing ML’s development environment, production systems, voracious appetite for data, and actionable output.
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