Presentation: Machine Learning from Theory to Practice
Abstract
With recent advances in computational power, machine learning is positioned to change the way we interact with the world around us. Likewise, a surge of well-maintained machine learning libraries has made it possible for engineers to use machine learning models with minimal background. However, many find that using machine learning responsibly in your company can be harder than it seems. Here, we will discuss some of the challenges that can arise when working with data.
Similar Talks
Psychologically Safe Process Evolution in a Flat Structure
 
            Director of Software Development @Hunter_Ind
Christopher Lucian
Not Sold Yet, GraphQL: A Humble Tale From Skeptic to Enthusiast
 
            Software Engineer @Netflix
Garrett Heinlen
Let's talk locks!
 
            Software Engineer @Samsara
Kavya Joshi
PID Loops and the Art of Keeping Systems Stable
 
            Senior Principal Engineer @awscloud
Colm MacCárthaigh
How Did Things Go Right? Learning More From Incidents
 
            Site Reliability Engineering @Netflix
Ryan Kitchens
Graceful Degradation as a Feature
 
            Director of Product @GremlinInc
Lorne Kligerman
A Dive Into Streams @LinkedIn With Brooklin
 
            Data Infrastructure @LinkedIn
Celia Kung
Liberating Structures @CapitalOne
 
            Agile Coach, Engineering @CapitalOne
Greg Myers
Making 'npm install' Safe
 
            Software Engineer @agoric
