Intelligent Cloud Operations
AI-powered observability for serverless applications. Troubleshoot and resolve problems quickly with sophisticated features such as anomaly detection, and root-cause-analysis. Save time and money with contextual awareness, end alert fatigues and remove data siloes while increasing productivity.
The Ultimate AI Solution for Microservices
CloudAEye root-cause analysis with context
Key Features and Benefits
Manage your cloud operations and boost your team productivity with the helpful AI augmented services.
Real-time insights about critical application issues without the constant tuning of configuration and rules. Setup in minutes
See AI-powered root-cause indicators from the logs automatically during an incident and avoid digging through logs
Detect & Fix Operational Issues
Automatic detection of lambda operational issues. Resolve issues quickly with ML-powered insights. See contextual information about the issue and recommendations on a possible fix
AI-powered anomaly detection of distributed tracing. Avoid digging through a large number of traces during an incident
AI-based root-cause analysis with context to reduce MTTR. RCA removes siloes and save time and money by expediting recovery from downtime
Current tools and processes aren’t ready for modern fast-paced cloud applications. Downtime and application issues translate directly into loss of revenue and adversely impact brand image. Curious if there is a better way to manage your cloud applications? Read this solution brief to learn more.
Supported AWS ServicesSee More
What Everybody is Saying
Monitoring serverless, actionable insights without the digging, root-cause indicators, fast recovery are some of the few things people are appreciative of.
Founder & CEO, Priyo
CloudAEye observability provided us with unparalleled visibility for our serverless functions. Onboarding took only a few minutes. The AI services were great to identify operational issues and provided timely insights. We saved valuable time with the AIOps workflow.
VP & Head of Engineering, AgriTech Startup
I like how CloudAEye is using AI to enable faster recovery times in production. Easier on-call and happier SREs!
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MLOps: Concept Drift
Machine Learning has proved to be a lucrative innovation, generating substantial profits for many organizations. In the context of machine learning, the term “concept drift” a.k.a. “nonstationarity“, “covariate shift” and “dataset shift” describes a phenomenon where the connections between input and output data in a given problem undergo changes over time.