Best Practices for Using Machine Learning in Businesses in 2018 

While some companies have been using machine learning for decades, there is a growing number of industries truly transformed by machine learning quite more recently. In the last 2-3 years numerous companies have been growing aggressively their data science teams and capabilities, while many other companies are just getting started with machine learning projects with the hope to extract significant business value from them. In this talk I will leverage my 10+ years of experience doing machine learning in California and the knowledge I acquired from countless discussions with seasoned machine learning professionals from the meetup communities I have been leading in Santa Monica. I will present some best practices in the form of 10 tips (a non-exhaustive list) of what companies should do if they want to get out the most from their machine learning projects. Some of these tips refer to machine learning software tools, some refer to data science processes and some to business/organizational structure. This talk is aimed to bring something useful and actionable to a wide range of audience, from companies embarking on their first machine learning projects to the seasoned professionals working in mature machine learning environments.

Pafka Szilárd
Chief Scientist, Epoch USA

Szilard studied Physics in the 90s and obtained a PhD by using statistical methods to analyze the risk of financial portfolios. He worked in finance, then more than a decade ago moved to become the Chief Scientist of a tech company in Santa Monica, California doing everything data (analysis, modeling, data visualization, machine learning, data infrastructure etc). He is the founder/organizer of several meetups in the Los Angeles area (R, data science etc) and the data science community website He is the author of a well-known machine learning benchmark on github (1000+ stars), a frequent speaker at conferences (keynote/invited at KDD, R-finance, Crunch, eRum and contributed at useR!, PAW, EARL etc.), and he has developed and taught graduate data science and machine learning courses as a visiting professor at two universities (UCLA in California and CEU in Europe).