Predicting Out Of Memory Kill events with Machine Learning (Ep. 203)

<p>Sometimes applications crash. Some other times applications crash because memory is exhausted. Such issues exist because of bugs in the code, or heavy memory usage for reasons that were not expected during design and implementation. Can we use machine learning to predict and eventually detect out of memory kills from the operating system?</p> <p>Apparently, the Netflix app many of us use on a daily basis leverage ML and time series analysis to prevent OOM-kills.</p> <p>Enjoy the show!</p> Our Sponsors <p>Explore the Complex World of Regulations. Compliance can be overwhelming. Multiple frameworks. Overlapping requirements. Let <a href='https://arcticwolf.com/datascience'>Arctic Wolf</a> be your guide. Check it out at <a href='https://arcticwolf.com/datascience'>https://arcticwolf.com/datascience</a></p> <p> </p> <p><a href='https://amethix.com/'>Amethix</a> works to create and maximize the impact of the world’s leading corporations and startups, so they can create a better future for everyone they serve. We provide solutions in AI/ML, Fintech, Healthcare/RWE, and Predictive maintenance.</p> <p> </p> Transcript <p>1 00:00:04,150 --> 00:00:09,034 And here we are again with the season four of the Data Science at Home podcast.</p> <p>2 00:00:09,142 --> 00:00:19,170 This time we have something for you if you want to help us shape the data science leaders of the future, we have created the the Data Science at Home's Ambassador program.</p> <p>3 00:00:19,340 --> 00:00:28,378 Ambassadors are volunteers who are passionate about data science and want to give back to our growing community of data science professionals and enthusiasts.</p> <p>4 00:00:28,534 --> 00:00:37,558 You will be instrumental in helping us achieve our goal of raising awareness about the critical role of data science in cutting edge technologies.</p> <p>5 00:00:37,714 --> 00:00:45,740 If you want to learn more about this program, visit the Ambassadors page on our website@datascienceathome.com.</p> <p>6 00:00:46,430 --> 00:00:49,234 Wel