Enable javascript in your browser for better experience. Need to know to enable it? Go here.
已发布 : Nov 30, 2017
不在本期内容中
这一条目不在当前版本的技术雷达中。如果它出现在最近几期中,那么它很有可能仍然具有相关参考价值。如果这一条目出现在更早的雷达中,那么它很有可能已经不再具有相关性,我们的评估将不再适用于当下。很遗憾我们没有足够的带宽来持续评估以往的雷达内容。 了解更多
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.

Radar

下载第25期技术雷达

English | Español | Português | 中文

Radar

获取最新技术洞见

 

立即订阅

查看存档并阅读往期内容