48 Wall St. - 15th floor, New York, NY
Join us at Dev Bootcamp on April 27th to learn about machine learning and why it's powerful. Hear from x.ai experts in tech talks: 1.Scaling out Machine Learning & 2.Sailing the Seas of Data
Talk #1 -Scaling Machine Learning @ x.ai
What is machine learning and why is it powerful? In this talk, Ben will go over some of the fundamental principles of machine learning and the distributed systems required to harness the power of machine learning models. Ben will also do a deeper look at how we use Spark at x.ai to generate features and why neural nets are a powerful modeling technique for x.ai 's problem space.
Ben is a data engineer at x.ai. At x.ai, Ben works on an artificial intelligence system that trains, tests, validates, and deploys machine learning models. For the past several years, Ben has worked at tech companies in Boston and New York. He is a self-taught engineer with a formal education in Politics and Philosophy. In his free time, Ben enjoys playing chess and poker and builds fantasy basketball machine learning algorithms.
Sailing the Seas of Data
When you're looking to start a career in data-oriented fields like machine learning, you can often encounter a buzzword bingo of hard to define terms: big data, data science, NLP, AI, deep learning, etc. All of this terminology really gets in the way of understanding what a person really needs to know and what they will really do once they get started working in this field. This talk will try to cut through that haze and give you a concrete picture of what it takes to be really valuable in a career in machine learning. It will cover the full lifecycle of jobs in machine learning from getting hired all the way to succeeding as a team.
Jeff Smith is the cofounder of John Done. For the past decade, he has been working on data science applications at various startups in New York, San Francisco, and Hong Kong. He’s a frequent speaker and blogger, and the author of “Reactive Machine Learning Systems,” an upcoming book from Manning on how to build real-world machine learning systems using Scala, Akka, and Spark.