For Data Conscious Companies
Lykuid’s advanced machine learning engine learns from the complex, interrelated patterns of behavior between cloud applications and services to provide powerful insights for high performing engineering teams. Legacy approaches to cloud monitoring use single dimension anomaly detection techniques or threshold settings to detect issues. These traditional approaches don’t take into account the high dimensionality of cloud infrastructure and the complex interactions between resources that drive most abnormal behavior.
Lykuid utilizes machine learning to solve the challenges faced within cloud and application monitoring. We combine unsupervised and supervised machine learning models to continuously learn what information and insight is most relevant to our users.
We understand just reducing noise isn’t enough. The need to make quick decisions based on real operational context is more important now than ever. At Lykuid, we interpret – and contextualize – insights from your data to provide quick access to the most important, relevant, and actionable intelligence.
Lykuid shows predictive recommendations related to your solution search. When visualizing data, Lykuid automatically recommends “solution-oriented” items related to an incident.
The growing user base that now uses Lykuid is becoming a community, a community all seeking to find, and use predictive answers to pressing engineering support issues. Lykuid’s platform provides recommendations that use that collective, community intelligence to go beyond simple correlations. This process leverages the entire base of Lykuid users to achieve the best outcomes for all.
Lykuid identifies hidden patterns of interrelated behavior across cloud resources, and provides actionable insights for high-performing engineering teams.
Lykuid uses adaptive machine learning to self-learn and self-optimize. This method eliminates the need to set and maintain alert thresholds manually.
Lykuid’s advanced machine learning engine identifies early indicators of impending issues so teams can take action before business is negatively impacted.