10 Machine Learning Issues that Nobody Talks About feat. Twitter

Dataiku and General Assembly will be hosting two talks exploring the best practices and common setbacks teams run into when building ML systems into their infrastructure.

Please RSVP on both Meetups and the GA website here: https://generalassemb.ly/education/bigger-problems-than-big-data-10-machine-learning-issues-with-twitter-cortex/new-york-city/70327

Priority will be given to those who have RSVP'd on both sites.

Tentative Schedule:
6:30pm: Pizza + Beer
7:00pm: DS Best Practices (at Scale) with Jordan Volz, Senior Data Scientist at Dataiku
7:30pm: Bigger Problems than Big Data: 10 Machine Learning Issues that Nobody Talks About with Dan Shiebler, Senior Machine Learning Engineer at Twitter Cortex

Abstracts:

DS Best Practices (at Scale) with Jordan Volz, Senior Data Scientist at Dataiku:
Although Data Science and Big Data are two worlds that are unwieldy on their own, their intersection has proven quite cumbersome for many businesses. In this talk, we will review some strategies for success in working with big and small data, common pitfalls in the data science process, building a collaborative data science experience, and how to overcome common obstacles when making the leap to large-scale data science.

Bigger Problems than Big Data: 10 Machine Learning Issues that Nobody Talks About with Dan Shiebler, Senior Machine Learning Engineer at Twitter Cortex:
In this presentation, we will explore the opportunities and growing pains of Machine Learning as a serious industry force. Through this exploration, we will learn how recent research in the Machine Learning space can enable large companies to become exponentially more productive in sharing and distributing Machine Learning models and insights. We will also see how Machine Learning systems can dramatically increase system complexity and technical debt.

Bios:

Jordan Volz is a Senior Data Scientist at Dataiku, where he helps customers design and implement ML applications. Prior to Dataiku, Jordan specialized in big data technologies as a systems engineer at Cloudera, and enterprise search technology as a technical consultant at Autonomy, frequently working with large financial organizations in the US and Canada. He holds degrees from Bard College and the University of Amherst, and is academically trained in pure mathematics.

Dan works at Twitter Cortex, where he develops Machine Learning Models that make sense of the world's data. In his spare time he works with the Serre Lab at Brown University to train neural networks to think like humans. Previously, Dan designed smartphone sensor algorithms for car insurance at TrueMotion.