Automated machine learning for autonomic computing

We are witnessing an explosion in the amount of data generated. Every server, device, and system is able to generate a stream of information that is both valuable and ever changing. It is becoming insufficient to simply store the data for later analysis and modeling. Instead there is a growing need to stream data to adaptive models and take instant action. This type of online system imposes hard constraints that the field of machine learning has not addressed. The systems must be highly automated, automatically adapt to changing statistics, deal with temporal data, and work well across a wide range of inputs. In this talk I will go over these issues and how they impact adaptive systems. I will describe a new technology for streaming analytics and illustrate how this technology works in a practical product called Grok. Using Grok I will show how streaming analytics can be appropriate for applications such as predictive maintenance, server capacity planning and cluster health monitoring. As the number of data sources increases, adaptive streaming solutions will play an increasingly important role in the future of autonomic computing.