Now, Kirsten Westeinde & Rich Jolly

Processed with VSCO with c1 presetUsing Your Data to Give Your Customers Superpowers

Kirsten Westeinde (@kmkwesteinde) is a technology enthusiast and a lifelong learner. Currently a senior software engineer at Shopify, where she solves challenging web development problems on the daily. She’s toiled and built apps and services with Ruby on Rails for greater than 5 years and is constantly adding new tools to her tool belt. In the important realm of building us humans, she is passionate about building diverse teams, mentoring & teaching, and gaining new perspectives through travel & cross cultural communication.

Being a software developer who recently started working on a machine learning project that serves over 600,000 merchants at Shopify, Kirsten had to quickly bootstrap her machine learning skills. Being the intelligent and capable soul she is, it hasn’t been all that complicated or scary for her! Certain problems are inherently good candidates to be solved with machine learning. She’s planning to start off by discussing how to identify them, including some real life examples from my organization. Once identified, she’ll guide the audience through how to define their problem as a machine learning model: identifying their features & labels. Once the model has been defined she will discuss how to “productionize” it, describing their current setup, how they got there, and some of the challenges the team faces: monitoring/alerting, “online” training, and changing model features as data changes.

This talk intentionally treats model training as a black box, as it is the most well documented step of the ML process. The most challenging and nuanced aspects are identifying problems, defining a model, and productionizing it. Kirsten will walk the audience through these steps in detail, using real life examples, and they should leave feeling better prepared to tackle some machine learning problems of their own.

richard-jollyBreaking the Barriers to ML in Your Organization

Rich (@rich_jolly) is VP of Data Science at the Hawes Group, a small/medium business supplying services and software products in the accounts receivable space. Rich lives in Portland and is excited to join in and support my community.

You’ve been hearing incredible talks for ML4ALL learning how to apply machine learning. Great. But, there’s another critical piece to the puzzle – now that you can do machine learning, how can you initiate a ML project in your organization? To the dismay of those working at Amazon, Apple, Google and Facebook, many companies, especially small and medium businesses, have not ‘seen the light’ yet on the value of ML. As you head back to work, your management may well barrage you with reasons why doing an ML project is not a good idea. Rich will supply you with ideas and more to get past each and every objection and get your organization on the path machine learned winning wins. Objections he’ll address:

  • But we don’t have the software or compute power to do that!
  • But we can’t afford to hire an army of PhDs!
  • BI, AI, ML, Analytics, I don’t even know how to think about it!
  • But what should we work on? Will it have an ROI (return on investment)?
  • Even if we have some success, how can we sustain it?


I’m getting ramped and am really looking forward to all of these solid speakers and topics at ML4ALL! To get your tickets navigate over to ML4ALL here. Also check out the previously introduced speakers: