SolarWinds Orion Alerting brings optimizations with the four golden signals: latency, errors, saturation, and traffic. These are your key signals to effective alert noise reduction. In this session, SolarWinds Distinguished Engineer Karlo Zatylny joins SolarWinds Head™ Geek Patrick Hubbard to discuss why these are the most important attributes and show you how to build better alerting by keeping them in mind.
Patrick Hubbard, Technical Product Marketing Principal and Head Geek, SolarWinds
An accomplished technologist with over 20 years of hands-on experience, Patrick’s career includes software development, operations, product management and marketing, technology strategy, and advocacy. An unapologetic market-hype deconstructionist, Hubbard is passionate about helping front-line technology professionals learn new skills to delight, not just satisfy, users. Hubbard’s current focus is helping enterprises adopt rational cloud-native strategies that deliver the transformation business increasingly demands. Since joining SolarWinds in 2007, Hubbard has combined his technical expertise with an IT customer perspective to drive product strategy, launch the Head Geeks™, develop and manage the SolarWinds Certified Professional® (SCP) and SolarWinds® Academy Training Classes programs, and create the SolarWinds online demo platform. Today, most admins recognize Hubbard as the executive producer of the Telly award-winning SolarWinds Lab™, and SolarWinds THWACKcamp™. With code in SolarWinds products and regular application of emerging technologies, Hubbard is a frequent blogger and invited speaker for cloud, DevOps, IT, security, data center, and networking conferences.
Karlo Zatylny, Distinguished Engineer, SolarWinds
Karlo battles the continued needs of data science and architecture at SolarWinds. Throughout his 11 years at SolarWinds, Karlo has been able to help engineering teams deliver solutions and help customers succeed in their ever-changing IT environments. His last three years at SolarWinds have been focused on the delivery of machine learning-based solutions delivering immediate value. He enjoys realizing the theory of machine learning in deliverable features by bridging the gap between the worlds of data science research and engineering feature implementation.