Machine Learning Ethics and Distributed Systems

This Month we will be meeting at Redfin's new location in Seattle with two separate talks on ML.

Sponsors:

Vertica provides an analytics platform for big data analytics and will be providing food for the event as well as giving a talk about ML on distributed systems

Acheron Analytics provides consulting for Machine Learning and Data Science and will be giving a talk about the ethics involved in Machine Learning.

Redfin is a full-service real estate company and will be hosting our event in their new office.

Schedule:

6:00 - Food and Networking

6:30 - Talks

8:00 - Networking, questions, etc

8:30 - Clean up, clear out stragglers

Instructions: Upon arriving at the Hill7 building, head to the elevator bank and press 6 on any of the kiosks. It will direct you to the appropriate elevator bay. You must then check-in at Redfin reception.

Parking: Parking is available in the garage off Boren Avenue, located between Hill 7 and the Hilton Garden Inn. Enter the driveway by following the Hilton Garden Inn signs. The garage entrance will be through the hotel’s valet drop-off area and on your right. There are also lots of paid parking lots near our office.

Talk 1: Ethical Machine Learning: Just because we can...Should we?

Abstract: Non-technical companies are slowly finding ways to increase their business value using the increased speed of computing and statistics. The problem is, business has always been more concerned about increasing the bottom line, vs. social impact. It is one thing when we joke about large e-commerce sites selling us that extra toaster. But what about when companies that have products that have been proven harmful reach out to data scientists and attempt to have them develop systems that increase the profit for a product that has a negative social impact, or when companies use data science to manipulate the customer, rather than benefit them. Should we? Is it right to forget about the social impact just to make an extra dollar?

Bio: Ben has 5 years of experience involving data and software engineering focused on data science, analytics and automation. He has worked on 4-5 major data science projects involving healthcare, finance, web scraping, operational,and biological data where he designed and deployed multiple systems from back to front. He co-founded Acheron Analytics because it gives our team the opportunity to help other corporations utilize the advantages data science and analytics provides.

Talk 2: Machine Learning on Distributed Systems

Abstract:

Most real-world data science workflows require more than multiple cores on a single server to meet scale and speed demands, but there is a general lack of understanding when it comes to what machine learning on distributed systems looks like in practice. Gartner and Forrester do not consider distributed execution when they score advanced analytics software solutions. Many formal machine learning training occurs on single node machines with non-distributed algorithms. In this talk we discuss why an understanding of distributed architectures is important for anyone in the analytical sciences. We will cover the current distributed machine learning ecosystem. We will review common pitfalls when performing machine learning at scale. We will discuss architectural considerations for a machine learning program such as the role of storage and compute and under what circumstances they should be combined or separated.

Speaker Bio:

Josh Poduska is a Senior Data Scientist in HPE’s Big Data Software Group. Josh has 16 years of experience as a practitioner in the analytical sciences with an emphasis on machine learning and statistical applications. He spent the last six years focusing on advanced analytical solutions with MPP columnar databases. At HPE he is part of the Vertica team and enjoys helping organizations solve their toughest data challenges.