The multiple challenges of operating ever more complex environments are well known. The most common we hear when we are speaking with our customers and partners are based around the vast amount of data now being produced and the quality of it. These aren't new problems when it comes to network availability and performance monitoring. When I started working in this area in the late 1990s, Network Operations Centers (NOCs) were already drowning in the amount of data being produced. Back then, in the early days of systems and network management solutions, data would simply be discarded to avoid overloading the network management system.

Thursday, October 10th at 10am PDT / 1pm EDT Machine Learning (ML) isn't just a buzzword anymore -- it's affecting how we communicate, shop, live and respond to critical IT incidents. While some IT and engineering leaders are concerned that implementing ML in their incident response processes will render their employees obsolete, others simply don't trust a machine to automate sensitive work. However, when implemented correctly, we believe ML can enhance -- not replace -- the work your teams are already doing, without requiring much or any effort on your part. To learn how, join us for a live webinar with the VictorOps Head of Data Science and resident Machine Learning expert, Will Stanton. We'll focus on: Demystifying AI, Machine Learning and AIOps: Definitions, anecdotes and fun facts to help you understand the technologies shaping the future of engineering and IT The meaning of "human-centered" ML: How VictorOps delivers insights to the end user exactly when and where they need it -- all while keeping them in complete control What makes ML work well: Ideal use cases for getting started with Machine Learning in incident response, effortlessly Demystifying AI, Machine Learning and AIOps: Definitions, anecdotes and fun facts to help you understand the technologies shaping the future of engineering and IT The meaning of "human-centered" ML: How VictorOps delivers insights to the end user exactly when and where they need it -- all while keeping them in complete control What makes ML work well: Ideal use cases for getting started with Machine Learning in incident response, effortlessly The meaning of "human-centered" ML: How VictorOps delivers insights to the end user exactly when and where they need it -- all while keeping them in complete control