Machine Learning and Data Science for Quality and Performance Engineering
Managing the quality and performance of complex systems requires more than simply executing test cases and running load tests. You need to perform careful analysis of test results and production metrics. The sheer amount of data generated in production and testing makes analysis a huge challenge that is often left wanting. With the magic of machine learning (ML) and the application of data science techniques, you have the opportunity to derive valuable and actionable information from big data. Gopal Brugalette shares the basic concepts behind ML, covering clustering, classification, and predictive analysis. He shows you how to implement algorithms using open source tools and languages like Python and R. With real-world examples, Gopal demonstrates the big data platforms Hadoop and Elasticsearch and illustrates concepts with quality and performance engineering problems like performance monitoring, test result comparisons, error message analysis, and user insights. Join Gopal to learn about data science and how you can start solving your quality and performance engineering challenges.
A performance architect/principal engineer, Gopal Brugalette has experience that spans eCommerce, financial, and various technology industries. He is developing a machine learning-based system for analyzing production and test data. Gopal’s responsibilities have included preparing sites for peak events, user behavior insights, DevOps transformations, applying data science and machine learning to performance problems, developing engineering frameworks, and expanding performance engineering activities into the development cycle and production. Previously, he was a researcher at the Center for Experimental Nuclear Physics. Gopal has presented at numerous industry events and been featured in online magazines. Outside of IT, he enjoys developing his permaculture farm and woodworking.