Reasonable Scale Machine Learning


May 31, 04:00PM PDT(11:00PM GMT).
  • Free 174 Attendees
Description
Speaker

Machine learning comes in many shapes and sizes but most of the conversation (around tools, techniques, division of labor) is dominated by a handful of companies, who do ML at a scale nobody else needs to. Of all the endless forms that companies outside of Big Tech can take, particularly interesting is a growing and underserved segment that is especially relevant for ML systems, known as reasonable scale companies, meaning reasonable along the axes of monetary impact, team size, data volume, and compute resources.

Jacopo and Hugo will discuss reasonable scale machine learning and what the majority of non-FAANG companies need to know in order to build out sophisticated ML functions, and we will also discuss what such machine learning actually looks like for businesses and practitioners alike, and what you can do to get started with reasonable scale ML today.

After attending, you’ll know:
- What types of companies can benefit from reasonable scale machine learning (hint: most)
- What types of data, tools, and talent you need in order to build a sustainable ML function
- Barriers to entry for reasonable scale machine learning, how you can get started today, and industry trends that will help you in your ML journey

The fireside chat will be followed by an AMA with Jacopo and Hugo at slack.outerbounds.co.


*The event is hosted by our partner, Outerbounds.
Jacopo and Hugo

Jacopo Tagliabue
Director of A.I. at Coveo, where they combine product thinking and research-like curiosity to build better data-driven systems at scale.
Hugo Bowne-Anderson
Head of Developer Relations at Outerbounds
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