Autonomous Anomaly Detection and Automation in Multi-Cloud Micro-Services environment


Cloud is now an integral part of running business and software’s with an increasing number of applications running on a microservices-based cloud system (such as AWS, GCP, IBM Cloud). It is challenging for the enterprises and cloud managed service providers to offer uninterrupted services with guaranteed Quality of Service (QoS) factors.

Existing monitoring frameworks often do not detect serious issues among a large volume of issues generated during the cloud system usage. The delayed response time proves damaging.

For our client who wanted to help companies manage the cloud better, we built an automated system to detect serious performance issues and execute their self-remediation.

Challenges and Opportunities

There were a whole host of challenges faced by our client :

  • Inability to detect performance anomalies, in real-time, through monitoring KPIs.
  • Maintenance of a Large team to manage the cloud.
  • Painfully long durations in root-cause tracing.
  • Lack of provision for triggering self-remediation actions.
  • Decrease the cost of managing cloud environments.
  • Breaking the siloed Domain expertise to boost the ability to support cross domain cloud platforms.
  • Minimizing the manual processes that drive cost of management.
  • To shift from traditional tools and teams presently used in operations management and optimization of cloud services.
  • To extend current automation platforms provisioning and configuration to operations management and optimization.

Our Solution

Here are the essential elements of our solution we built :

  • AI-based Automated Anomaly Detection – Our platform provides an automated prediction-based anomaly detection and localization system, capable of detecting performance anomalies of a microservice using machine learning techniques and determining their root-causes using a localization process.
  • Remedial Action – The system can intelligently identify remedial actions to fix anomalies based on an intelligent domain experience learning system.
  • Intelligent Bot – Anomaly detection and remediation, compliance enforcer, migration assistance, and multi-cloud hope.
  • AIops – ML models for anomaly detection and clustering, Natural Language Processing for context awareness, Machine learning driven tracing.
  • Integrated data ingestion – from multi cloud environments.
  • Integrated CI/CD pipeline analytics and automation of deployment
  • Dynamic application tracing across cloud native environments.
  • Autonomous Compliance management

Technology Stack


React Js



SQL Server



Agile Methodology





Impact and Business value

  • The value creation via our platform was direct and significant.
  • The platform made the management of a multi-cloud environment a breeze.
  • There was a reduction in the cost of managing the cloud as it required fewer people to be on the watch.
  • Indirect value creation bye prevention of application failure and functioning.
  • Seamless user experience due to real-time identification and remediation of issues.
  • Greater confidence in launching upgraded as the AI-enabled anomaly detection provided instrumental assistance to the IT team.

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