#201: As an application developer, we’re used to adding logging to our applications. We also work with our operations counterparts to enrich those logs to help them out when troubleshooting. But what happens during an incident when the logs are flowing so fast that neither you nor the operations people can keep up? That’s where machine learning can help.
In this episode, we speak with Ajay Singh, CEO at Zebrium, about why humans need help troubleshooting issues and how machine learning helps detect outliers and solve those last mile problems.
Ajay Singh is a strong advocate for creating products that “just work” to address real-life customer needs. As Zebrium CEO, he is passionate about building a world class team focused on using machine learning to build a new kind of log monitoring platform.
Darin Pope is a developer advocate for CloudBees.
Viktor Farcic is a member of the Google Developer Experts and Docker Captains groups, and published author.
His big passions are DevOps, Containers, Kubernetes, Microservices, Continuous Integration, Delivery and Deployment (CI/CD) and Test-Driven Development (TDD).
He often speaks at community gatherings and conferences (latest can be found here).
He has published The DevOps Toolkit Series, DevOps Paradox and Test-Driven Java Development.
His random thoughts and tutorials can be found in his blog TechnologyConversations.com.
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