Technology Fridays: SignifAI Enables Machine Intelligence for DevOps
Welcome to Technology Fridays in 2018! Last year, we started this section to explore the products and platforms that are innovating in technology markets but that are, somehow, still flying under the radar. Today, we are going to cover SignifAI, a platform that is leveraging machine learning to improve TechOps solutions.
SignifAI represents the next evolution of the application performance monitoring(APM) and DevOps tools spaces. Both markets have seen tremendous levels of innovation in recent years with platforms such as NewRelic and AppDynamics leading the charge in the APM space and technologies like Chef or Puppet redefining the DevOps ecosystem. While those platforms have drastically improved the capturing and visualization of IT data, they are still missing the foundation to translate the data into relevant knowledge. That its where SignifAI comes in. The platform provides a layer of machine intelligence(MI) that brings it closer to the way human experts troubleshoot and manage IT operations.
Conceptually, SignifAI brands itself as a TechOps as a service but its much more than that. The platform literally expands traditional APM and DevOps solutions with machine intelligence models that enable the rapid diagnosis and resolution of software and infrastructure errors. SignifAI accomplishes that by combining two main capabilities: sophisticated data capture and fast root cause analysis.
Data collection is enabled in the SignifAI platform via Sensors which integrate with different types of software and infrastructure platforms. Active Inspectors are a type of Sensor that collects information by integrating with products through their specific APIs. For instance, the AWS Active Inspector provides data collection and analysis across different AWS services such as Lambda, RedShift, SQS, DynamoDB, ElasticCache and many others. Additionally, the AWS Active Inspector is able to receive events from AWS CloudWatch monitoring service which allow organization to implement SignifAI without altering their AWS architecture. In addition to AWS, SignifAI provides Active Inspectors for platforms such as DataDog, Grafana, Akamai among many others.
The other type of Sensor provided by SignifAI is known as Web Collectors which enable the integration with applications via webhooks. The list of Web Collectors include well known APM platforms such as AppDynamics, NewRelic or Splunk.
The SignifAI Control Center is the main user interface to interact with the data produced by the platform. The Control Center prioritizes the issues and alerts generated by the platform based on the specific customer needs. That level of prioritization is possible because of the Site Reliability Engineer Augmented Member or SAM which is an MI agent completely tailored to a tenant’s environment. SAM uses machine learning algorithms to understand the characteristics of an environment and provide personalized answers to specific issues. SAM’s intelligence improves as more data and activity gets into the platform which makes it almost as an intelligence digital assistant extension to your TechOps team.
I tend to see SignifAI as the next evolution of APM platform. As a result, SignifAI is likely to experience competition from APM platforms such as NewRelic or AppDyanmics that are venturing into the MI space. Additionally, the APM services or platforms such as AWS or Azure can also be seen as a relevant competitor of SignifAI.