The amount of data collected by IT operations has been increasing for years. For example, the trend toward microservices means that more applications are generating their own operational data, and tools such as Splunk, Prometheus, or the ELK stack make it easier to store and process data later on, to gain operational insights. When combined with increasingly democratized machine learning tools, it’s inevitable that operators will start to incorporate statistical models and trained classification algorithms into their toolsets. Although these algorithms have been available for years, and various attempts have been made to automate service management, we're only just starting to understand how machines and humans can collaborate to identify outages earlier or pinpoint the source of failures. Although there is a risk of overhyping Algorithmic IT operations, steady improvement in machine learning algorithms will inevitably change the role of humans in operating tomorrow's data centers.
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