Traditional IT monitoring requires users to decide which metrics to monitor and manually set individual thresholds. With modern IT ecosystems, the number of available and relevant metrics can easily get to tens of thousands. Hence configuration becomes unmanageable and gaps in coverage grow while getting meaningful insight and reliable monitoring becomes impossible.
AIMS applies automation, machine learning, and artificial intelligence to solve this problem. AIMS generates a deep, dynamic 360° map of your entire IT environment and data – including the unknown unknowns.
The adoption of Microsoft Azure and the public cloud, in general, grows quickly. There are obvious advantages from moving traditional servers to virtual machines in Azure, such as provisioning and administration of the servers.
Adopting microservices and elastic resources represent a material change from a traditional server-based or monolithic approach and with different benefits and advantages.
Read the complete guide to the future-proof Azure monitoring: 10 key considerations, challenges, and solutions.
Proactively identify issues that can impact your business
Extract real-time business intelligence from IT systems
Enable IT to impact business outcomes and drive innovation
Prevent bottlenecks and downtime in critical business processes
Automatically detect changes to resource consumption & control costs
Reduce time wasted on manual configuration and troubleshooting
With 200+ out-of-the-box integrations, the flexible AIMS API, and agents you can connect all your systems - cloud, hybrid, and on-premise. AIMS automatically normalizes the data with the hyper-scalable time series Normalization Engine. Then AIMS uses machine learning and artificial intelligence to learn the behavior of all your performance metrics across your IT environment, identifies critical correlations and dependencies, and provides you early notification of IT issues that can bring down your business.
With 200+ out-of-the-box integrations, the flexible AIMS API and agents you can connect all your systems - cloud, hybrid and on-premise.AGENTS AND INTEGRATIONS
Automatically normalize all your time-series performance data with a hyper-scalable Normalization EngineDISCOVER DATA NORMALIZATION ENGINE
The AIMS ML engine learns behavior based on millions of data points across your IT environment and identifies correlations and dependencies.LEARN MORE ABOUT TOPOLOGY
AIMS Anomalies leverage AI and machine learning to provide early notification of IT issues that can bring down your business.LEARN MORE ABOUT ANOMALY DETECTION
AIMS AIOps Dashboards automagically surface key insight across your on-premise, cloud, or hybrid environments.VIEW DASHBOARDS AND ANALYTICS
Gain control of complex and challenging IT environments, act on early warnings to avoid business impact.ACT ON EARLY WARNINGS
Are you running on Azure? Read the complete guide to the future-proof Azure monitoring: 10 key considerations, challenges, and solutions.
AIOps can be broken down into key capabilities, or building blocks, from collecting data to taking action - each one delivering incremental value.
What if the technology dependencies and relationships could be surfaced and kept up-to-date without a massive project and ongoing maintenance?
In this e-book, we deep dive into the key components, variants, fundamental differences between approaches, and conclude with what you need to consider before implementing Anomaly Detection.
AIOps is a new buzzword that has emerged in IT. As with any other buzzword you can think of, every vendor has its own definition. But when you cut through all the marketing, what really is "AIOps"?
How do anomaly detection and machine learning impact the operating model of IT departments? Learn first-hand from industry-leading experts