Join us at Dev Bootcamp on April 27th to learn aboutmachine learning & 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 & why is it powerful? In this talk, Ben will go over some of the fundamental principles of machine learning & the distributed systems required to harness the power of machine learning models. Ben will also do a deeper look at how we use Spark atx.aito generate features & why neural nets are a powerful modeling technique forx.ai's problem space.
Bio
Ben is a data engineer atx.ai. Atx.ai, Ben works on an artificial intelligence system that trains, tests, validates, & deploys machine learning models. For the past several years, Ben has worked at tech companies in Boston & New York. He is a self-taught engineer with a formal education in Politics & Philosophy. In his free time, Ben enjoys playing chess & poker & builds fantasy basketball machine learning algorithms.
Talk #2
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 & what they will really do once they get started working in this field. This talk will try to cut through that haze & 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.
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Jeff Smith is the cofounder ofJohn Done.For the past decade, he has been working on data science applications at various startups in New York, San Francisco, & Hong Kong. Hes a frequent speaker & blogger, & the author of Reactive Machine Learning Systems, an upcomingbook from Manning on how to build real-world machine learning systems using Scala, Akka, & Spark.