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The 3 key concepts in Machine Learning operations

As data sets compiled by organizations increase in size and complexity, it becomes impractical to manually detect infrastructure problems, intruders or business problems.

Simply, there are not enough available human resources in companies to observe each potentially interesting metrics during the whole day. Although it is possible to create some graphs showing system performance, it is almost impossible to identify unusual behaviors and anomalies in all data in real time. And due to the fact that businesses are global, this is a 24/7 responsibility for IT teams, which need visibility of these operations at all times.

That is why many companies require a partner to help identify anomalies and atypical values in their business great ecosystems. Through Predictive Monitoring services and the MLOps technology, companies have the capacity of monitoring and solving operating problems, improving their cybersecurity systems, detecting frauds and much more.

If you look forward to obtaining the most of Predictive Monitoring services, please pay attention to the following key factors.

    Due to the variety of elements which a great number of data daily generate, it is necessary to rely on the monitoring of all variables through the 4 big monitoring groups and to draw up dependency maps of business processes, so as to be able to effectively correlate and eliminate false positives. With this, it is possible to improve the learning of non-supervised algorithms and be more precise on anomaly predictions.
    Many organizations have resorted to data scientist teams, an expensive and scarce resource, to address all these difficulties. Although data scientist teams might relieve part of the heavy load, they normally analyze historical data manually analyzing groups without connection, and with personalized solutions which are only applicable to the limited data being examined. That is why, with non-supervised algorithms specialized for IT operations, it is possible to break away from data scientists and take advantage of the MLOps technology.
    Driven by futuristic applications for machine data (autonomous vehicles and intelligent networks), automatic learning has become a fashionable word in suite C. But there is a big gap between these concepts and the reality of what it is possible in the short term. Avoid falling into the trap of thinking too big and creating a very high standard to be entered by yourself. Perfect is the enemy of good.

Companies are consuming enormous quantities of online data flows, and that, by itself, might be felt as an important achievement. But data volume is not the end. The real question is: And now what? Make it a priority to obtain visibility of these data flows; otherwise, none of these consuming data works matter. Ask yourself which is the use case. The initial use case should not be something massive like cybersecurity. Instead, so as to focus on the appropriate use case, ask yourself: “Which is the value of all these data for the business? What can we learn from it?”
Let’s take, for example, Windows event registry. A viable use case goes beyond simply answering: “What is happening with these machines?” The real value comes with the capacity of detecting the user’s anomalous behavior, and identifying what is happening, when and where. Only with this deeper vision it is possible to create, step by step, the basis to administer operations better, identify cyber threats and reduce fraud. And as time goes by, it would be possible to be in the position of pursuing bigger hopes.

By Jaime Castro, ANIDA’s Service & Solution Sales Specialist.

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