When you first set up your Anodot account, you create alerts on the KPIs that matter most to you. Advanced alert configurations enable you to define various parameters so that you only get alerts that are important to you: selecting the metric, building a query, grouping the data by dimensions, selecting triggers and conditions, choosing who and where it should be sent to, and so on.
With AI analytics slated as the biggest disruptor to big data and analytics, data leaders are quickly integrating this capability into their data strategy. Itzik Feldman, data engineering manager at Atlassian, the enterprise software company responsible for Jira and Trello, recently credited Anodot with helping keep the company’s 3,000 employees in touch with product performance and customer experience.
August 2019 was a bad month when it came to financial services outages, with both consumers and investors faced with banking glitches that made them unable to access their accounts. Let’s take a look at the anomalies that made headlines over the last 30 days.
In our recent webinar on what it takes to build time series anomaly detection, industry experts Arun Kejariwal, Ira Cohen and Ben Lorica shared valuable advice for ways to successfully implement and execute anomaly detection systems in today’s increasingly complex corporate world.
There’s never a dull moment in the world of anomaly detection. These are the glitches that the world’s largest organizations battled in the month of July.
A recent report by Gartner casts light into the world of AIOps, and the need for deploying it in organizations today. AIOps is a modern approach to DevOps which is based on recent AI technology. Gartner’s vision of the AIOps platform is one that enables continuous insights across IT operations management.
There used to be a distinct, technical separation between terms such as AI and machine learning (ML) – but only while these technologies remained largely theoretical. As soon as they became practical in the real world, and then commodifiable into products, the marketers stepped in. Widespread overuse of the terms AI/ML in marketing have managed to thoroughly confuse the meanings of these words.
If you happen to be running multiple clusters, each with a large number of services, you’ll find that it’s rather impractical to use static alerts, such as “number of pods < X” or “ingress requests > Y”, or to simply measure the number of HTTP errors. Values fluctuate for every region, data center, cluster, etc. It’s difficult to manually adjust alerts and, when not done properly, you either get way too many false-positives or you could miss a key event.
