Companies struggle with the amount of data they collect every day, as well as how to derive insights from it. Now, InsightFinder helps solve these problems by offering distributed federated learning as a scalable and cost effective way for businesses to analyze their data at scale. Combined with InsightFinder’s proactive anomaly detection, incident prediction, and prevention technology, this approach enables opportunities to scale in new and powerful ways.
InsightFinder’s federated learning technology is beneficial for businesses in three main ways:
– Cost – InsightFinder’s anomaly detection can be performed locally, and only anomalies found are sent back to the central processing center.This means only anomalous events are needed to analyze, thus saving the amount of data that needs to be sent across networks.
– Privacy – Because data is analyzed on local servers, it does not need to change hands, and thus can remain private. Only anomalous data is sent back to InsightFinder to be further analyzed for root cause analysis, incident prediction, and prevention.
– Scale – Federated learning allows companies to obtain InsightFinder insights across geo-distributed systems such as IoT systems in a cost efficient way. In addition, as data is analyzed across disparate places – including on-prem and cloud environments, companies reduce the risk of single point of failure. That way, if one server does not perform, the federated learning system will continue running and analyzing in a distributed network capacity.
Let’s take a deeper look….
Federated Learning is a machine learning technique that allows environments to use specific algorithms and process data locally. InsightFinder’s federated learning works like this: our patented unsupervised machine learning technology is deployed to edge nodes called edge engine. This edge engine performs anomaly detection locally and sends the anomalies to the central processing entity, known as the core engine. The core engine then takes the analysis from edge engines and other data sources to perform root cause analysis and incident prediction.
An InsightFinder financial client has multiple data center environments hosting various types of applications across the world. They also have backup servers for this information. Because all this data needs to be processed locally, Insightfinder deployed edge engines in different clusters to extract data insights to be sent to the core engine for overall analysis. Because they leveraged federated learning, the customer reduced the amount of data it needed to send across the network by over 90%.
This is only the beginning for federated learning and AIOps. Find out how InsightFinder’s new offering could help your business by signing up for a free trial.