Atlassian, the software company behind Jira and Trello, credits Anodot with keeping their employees in touch with product performance and customer experience.

As companies realize the benefits they can achieve from automated outlier detection rather than with the manual dashboard monitoring and alerts used in traditional BI tools, they’re faced with the immediate question: build our own system or buy one?

When there's too much data to monitor manually, automated anomaly detection is a MUST for preventing data quality issues.

Because opportunities can open and close very quickly on social media, your anomaly detection needs to track and correlate data in real time.

In this webinar, we’ll cover everything you ever wanted to know about time series anomaly detection, and how you can make it work for you.

Learn more about the different types of outliers and why knowing these is key for correct outlier analysis.

One of the problems with statistical outliers is that they can be difficult to detect within the context of time series data. In this post, we’ll discuss how we can tune automated detection systems so that they become integral when it comes to identifying outliers across thousands to billions of metrics.

A recent Gartner report on AIOps, a fast-growing area of technology fusing IT operations and analytics for automated IT management, mentions Anodot as an established AIOps provider.

How do we track and monitor the performance of machine learning algorithms? Simple - with machine learning algorithms.