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Artificial Intelligence for Smarter Cybersecurity

#artificialintelligence

Organizations continue to embrace the Internet of Things (IoT), the cloud, and mobile technology. This has influenced considerable changes in the threat landscape and created more vulnerability points. Cybercriminals are leveraging these new vulnerability points to develop and launch sophisticated, high-volume, multi-dimensional attacks. Such attacks mean that data is at risk, and organizations must analyze potentially malicious files. Using artificial intelligence software, organizations can process large volumes of threat data and adequately prevent and respond to breaches and hacks.


Moving from Data Science to Machine Learning Engineering - KDnuggets

#artificialintelligence

For the last 20 years, machine learning has been about one question: Can we train a model to do something? Something, of course, can be any task. Predict the next word in a sentence, recognize faces in a photo, generate a certain sound. The goal was to see if machine learning worked, if we could make accurate predictions. What can we build with these models, and how can we do it?


Reverse engineering learned optimizers reveals known and novel mechanisms

arXiv.org Machine Learning

Learned optimizers are algorithms that can themselves be trained to solve optimization problems. In contrast to baseline optimizers (such as momentum or Adam) that use simple update rules derived from theoretical principles, learned optimizers use flexible, high-dimensional, nonlinear parameterizations. Although this can lead to better performance in certain settings, their inner workings remain a mystery. How is a learned optimizer able to outperform a well tuned baseline? Has it learned a sophisticated combination of existing optimization techniques, or is it implementing completely new behavior? In this work, we address these questions by careful analysis and visualization of learned optimizers. We study learned optimizers trained from scratch on three disparate tasks, and discover that they have learned interpretable mechanisms, including: momentum, gradient clipping, learning rate schedules, and a new form of learning rate adaptation. Moreover, we show how the dynamics of learned optimizers enables these behaviors. Our results help elucidate the previously murky understanding of how learned optimizers work, and establish tools for interpreting future learned optimizers.


Machine Learning Engineering Manager (Growth), Cash App

#artificialintelligence

Cash App is the fastest growing financial brand in the world. Built to take the pain out of peer-to-peer payments, Cash App has gone from a simple product with a single purpose to a dynamic money app with over 30 million active monthly users. Loved by customers and by pop culture, we've held the #1 spot in finance on the App Store for almost two years, and our social media posts see more engagement in a day than most financial brands see in a year. With major offices in San Francisco, New York, St. Louis, Portland, Kitchener-Waterloo, Toronto and Melbourne, Cash App is bringing a better way to send, spend, and save to anyone who has ever sought an alternative to today's banking system.


Designing a Python interface for machine learning engineering

#artificialintelligence

In order to do machine learning engineering, a model must first be deployed, in most cases as a prediction API. In order to make this API work in production, model serving infrastructure must first be built. This includes load balancing, scaling, monitoring, updating, and much more. At first glance, all of this work seems familiar. Web developers and DevOps engineers have been automating microservice infrastructure for years now.


Machine Learning Engineering Manager – Spotify Jobs

#artificialintelligence

The Personalization team makes deciding what to play next easier and more enjoyable for every listener. We built them by understanding the world of music and podcasts better than anyone else. Join us and you'll keep millions of users listening by making great recommendations to each and every one of them. The Lifetime Value team within Personalization is looking for an experienced Engineering Manager in New York City. You will be contributing to a highly scaled ML platform that will be used to evaluate the ROI of critical company bets, drive value-based recommendations and decisions, and predict complex user behaviors in collaboration with business leads, product managers, and data scientists across many business units. The long term goal of the team is to apply best-in-class technology and research to drive value for both our users and our business.


Machine Learning Engineering Manager

#artificialintelligence

The Personalization team makes deciding what to play next easier and more enjoyable for every listener.


Moving from data science to machine learning engineering

#artificialintelligence

For the last 20 years, machine learning has been about one question: Can we train a model to do something? Something, of course, can be any task. Predict the next word in a sentence, recognize faces in a photo, generate a certain sound. The goal was to see if machine learning worked, if we could make accurate predictions. What can we build with these models, and how can we do it?


Installing Kubeflow On Ubuntu

#artificialintelligence

In 2017, I built this deep learning machine as a solution to my growing AWS bill. In 2020, I struggled to get this machine to run Kubeflow locally. Here are the instructions that worked for me. Some of these instructions are based on this Ubuntu.com MicroK8s is a small version of Kubernetes that will work well locally.


Why we built a platform for machine learning engineering -- not data science

#artificialintelligence

About a year ago, a few of us began working on an open source machine learning platform, Cortex. Our motivation was simple: Building an application out of a model was a terrible experience full of glue code and boilerplate, and we wanted a tool that abstracted it all away. While we're very proud of our work on Cortex, we are just one piece of a trend we've seen accelerate over the last year, and that is the growth of the machine learning engineering ecosystem. Companies are hiring MLEs faster than ever, and the projects being released are getting better and better. While this is very exciting to us, we still frequently hear the question "What is machine learning engineering?"