Jasmin Nakic and Samir Pilipovic examine the application of a linear regression predictive model on time series performance data, discussing and evaluating different models to find the optimal choice for a given dataset. All steps will be supported with Python-based scripts so that you can easily implement similar models on your own data. You’ll also learn how to analyze performance data and business data from other sources to detect anomalies in important performance metrics and generate alerts with dynamic thresholds, taking into account weekly and seasonal spikes in traffic, as you explore the process from data collection to applying predictive models to evaluating and visualizing results.
This tutorial is sponsored by Salesforce and is open to all Velocity Conference pass types.
Jasmin Nakic is lead software engineer on Salesforce’s Frontier Scale performance team, where he helps analyze enterprise customer workloads and simulate large-scale benchmarks. Jasmin focuses on bringing advanced predictive analytics and machine-learning methods to massive amounts of system performance data. Previously, he did database and application development at Teradata, KickFire, KLA-Tencor, and Oracle. Jasmin started his computer science adventure in high school in his small hometown in Bosnia, where he wrote short programs to analyze results from astronomical observations on HP calculators. Later, at the University of Sarajevo, he studied system programming, compilers, advanced architectures, and networks and wrote a thesis on object-oriented approaches to database programming.
Samir Pilipovic is a senior software engineer on the Performance Engineering team at Salesforce. His primary interest is performance and optimization of large enterprise deployments in the cloud.
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