57:37
Java at Speed
Gil Tene talks about getting the most of Java applications and understanding some of the optimizations the latest crop of JVMs are able to apply when running on the latest servers.
49:11
Microservices & Scaling of Rational Interactions
Mark Burgess talks about the scaling of microservices in computer and human interaction.
40:53
Functional Performance
Martin Thompson discusses the most relevant laws governing software performance in the context of financial applications.
55:02
Continuous Optimization of Microservices Using ML
Ramki Ramakrishna shares Twitter’s recent experience in applying Bayesian optimization to the performance tuning problem, discussing a service used for continuously optimizing microservices.
35:16
Real-Time Monitoring with Grafana, StatsD and InfluxDB
Artur Prado presents a number of monitoring tools, how to use them and some code involved in tracking certain events.
44:29
Understanding Python Memory at Instagram
Min Ni discusses how Python memory profiling is done at Instagram, insights from memory profiling data, and learnings from tuning and improving Python memory garbage collection.
32:07
Caching for Microservices - Introduction to Pivotal Cloud Cache
Pulkit Chandra discusses how to use Pivotal Cloud Cache and its performance under load, demoing a Spring Boot app which uses Spring Data Geode to talk to a Pivotal Cloud Cache cluster.
54:41
NDBench: Benchmarking Microservices at Scale
Vinay Chella and Ioannis Papapanagiotou discuss Netflix's Cloud benchmark system, how it was integrated with their release cycle, showcasing how multiple instances can be monitored from a single UI.
46:44
Performance beyond Throughput: An OpenJ9 Case Study
Marius Pirvu talks about the new advancements in the area of JVM performance using the latest open source JVM technology at Eclipse OpenJ9 running with OpenJDK.
43:36
Getting Data Science to Production
Sarah Aerni covers the nuts and bolts of the Einstein Platform, a system that enables the automation and scaling of Artificial Intelligence to 1000s of customers, each with multiple models.
46:42
ML for Question and Answer Understanding @Quora
Nikhil Dandekar discusses how Quora extracts intelligence from questions using machine learning, including question-topic labeling, removing duplicate questions, ranking questions & answers, and more.
51:53
Go Programming Language
Dave Cheney discusses the Go language: writing and interpreting benchmarks, using performance tools built into the Go runtime, GC and writing GC-friendly code.
