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已发布 : Nov 30, 2017
Not on the current edition
This blip is not on the current edition of the Radar. If it was on one of the last few editions it is likely that it is still relevant. If the blip is older it might no longer be relevant and our assessment might be different today. Unfortunately, we simply don't have the bandwidth to continuously review blips from previous editions of the Radar Understand more
Nov 2017
评估 ? 在了解它将对你的企业产生什么影响的前提下值得探索

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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