As part of our series on applied machine learning workflow, we'll look at where data comes from, and how we make sense of huge collections of data.
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Naïve Bayes classifier is able to predict a distribution over a set of outcomes and is a good approach for enabling enable IT Operations Analytics tools to check whether an event is expected or not.
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Apriori algorithm is used for frequent pattern mining and association rule learning over transactional databases and is a good approach for enabling IT Operations Analytics tools to detect frequent patterns, and identify critical changes.
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As part of our series on applied machine learning workflow, we'll look at how to define your problem, and to specifically focus on what to fix for early and more efficient resolution of operational issues.
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Google cloud users experienced a brief outage, leaving users scratching their heads about what was happening to their Google services. Google attributed the root cause to a configuration change introduced to the network stack.
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With Machine Learning projects following a knowledge discovery process, we'll explore the recommended approach to an applied machine learning workflow that you can follow for thinking in a more focused way to tackle data-driven problems.
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k-NN stores is used for both classification and regression problems, and works well for dynamic environments that require frequent updates, making itan attractive approach for anomaly and outlier detection.
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Machine learning can be applied to solve really hard problems. While Machine learning can't be applied to everything, we'll look at the different approaches for applying Machine Learning and what problems can be solved.
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System outages are inevitable when working in IT. A prime example of system failure for exploring is what went down at Jurassic Park. Look at the change and incident management blunders that contributed to the park's failures, and how this applies to IT Operations.
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Lizard Squad takes down Facebook? No! Facebook suffered a brief, worldwide outage today. Attributing the cause to a configuration change, this raises the question for IT operations in general: when an incident occurs, how quickly can you know "what changed"?
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