Jan 17, 2023
Managing the performance of complex systems requires more than simply running load tests. You need to perform a careful analysis of test results and production metrics. The sheer amount of data generated makes analysis a challenge that is often left wanting. With machine learning (ML) and the application of data science techniques, you can derive valuable and actionable information from big data.
In this episode, Gopal Brugalette, a Sr. Performance
Architect/Manager/Software Engineer/ shares the basic concepts
behind ML, covering clustering, predictive analysis, and neural
networks.
He shows you how to implement algorithms using open-source tools
and languages like Python, R, and AWS cloud services. With
real-world examples, Gopal demonstrates the big data platforms
Hadoop, Elasticsearch, and AWS Sagemaker. He illustrates
performance engineering problems like performance monitoring, test
result comparisons, error message analysis, and user insights.
Also, Performance testing is a critical skill that is becoming increasingly important for organizations.
Still, unfortunately, it is not a skill many testers
possess—having an application that is not performant and not
meeting your customer's expectations can be dangerous to your
company's reputation and bottom line. Sometimes, it can lead to
layoffs as organizations try to cut costs.
I believe that anything that can be automated in the SDLC, even if
it's not a functional test, should be handled by testers and
automation engineers. As a tester, you might think you don't need
to know performance testing, but trust me, you do. A tester with
multiple testing skills is much more valuable to the company.
That is why I'd like to invite you to this year's online
Automation Guild.
The last two days of the conference (part of the 5-day ticket)
include many sessions on performance testing.
Register Now: https://guildconferences.com/ag-2023/