Machine Learning: Why the Hype and How to Leverage it in your Company

Hosted by ProductBC

Public group


Please join us for "Machine Learning: Why the Hype and How to Leverage it in your Company". We will be discussing advancements in Machine Learning and what this means for the future. Some points we will cover include the following:

- Which types of data sets that lend themselves well to Machine Learning
- What drives complexity (and cost) for integrations
- Where you may be able to find low hanging fruit for potential integrations
- How to get management support
- When to hire a data scientist
- How to setup a data science team for success
- How to increase productivity of an existing data science team

* This is a discussion and we want your active participation! *

There will be a presentation component to start but we hope that people will bring their curiosity and questions to help foster a lively discussion. If you would like to know where your idea for a falls on the complexity scale for implementation, this would be the discussion to find out!

The discussion is intended for:

- People who have not implemented anything with Machine Learning (but may be keen to do so)
- People who have started or even completed an implementation
- People who have only heard the term Machine Learning
- And anyone else who is curious about the points above.

* Speaker Bios *

Paul Save

Paul is a Product Manager for Central 1 Credit Union where he is in the customer discovery phase of a machine learning solution to retain members by combining the probability of attrition along with segmentation to align marketing outreach efforts. Previously, he was on a cyber security team at Central 1 where he prevented fraudulent actors from accessing member accounts. In his spare time he has presented on machine learning at various Product Camps and Meetups and participates in Kaggle (machine learning) competitions. He also volunteers as Vice President for the Product Management Association of BC and holds a masters of applied science in civil engineering from UBC.

Aleksey Nozdryn-Plotnicki:

Aleksey is a Data Scientist. He researches and develops products where Machine Learning delivers the key value proposition, often computer vision applications processing 3D scans and/or standard images. Aleksey is a Kaggle “Competition Master” following gold medal finishes in a trio advanced research-grade competitions from the 2017 Neural Information Processing Systems Conference relating to fooling state-of-the-art artificial intelligence computer vision models. He has a background in mathematics and a masters degree in Operations Research from UBC.

Mike Irvine

Mike is currently a postdoctoral research fellow at the Institute of Applied Mathematics, University of British Columbia and the British Columbia Centre for Disease Control. He is investigating modelling the impact of testing on the HIV epidemic, and interventions to combat the opioid overdose epidemic using a variety of Bayesian approaches. His PhD was focused on spatial pattern analysis and inference and is currently leading a machine learning project to emulate large-scale, computationally heavy models.