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Machine Learning for Machine Data
Adam Oliner and Jacob Leverich
Machine learning is a process for generalizing from examples. In this hands-on tutorial, we'll apply state-of-the-art methods to data from production systems to perform a variety of tasks relevant to SREs, including identifying anomalous systems, classifying messages by their content, and forecasting system state.
This will be a practical tutorial, aimed to instruct both about the capabilities of machine learning as well as its limitations. Machine learning topics include anomaly detection, classification, and clustering. We assume no prerequisite knowledge of machine learning, though some familiarity with statistics and linear algebra will be helpful.