Abstract:
MLOps has emerged as a powerful set of tools and strategies for effectively deploying Machine Learning models into production. As an ever-growing number of organizations look to deploy ML models they face an expensive question: how much is this all going to cost? While the research community is chasing the latest and greatest, both data and models are getting bigger and more compute intensive. This trend has led to a ballooning of costs, especially as more models are being deployed into the cloud. In this talk, I will share several effective methods to reduce costs when deploying Machine Learning models. Namely, we will zoom-in on two key areas for cost reduction: models and infrastructure.

What You’ll Learn:
Most of the ML community is chasing the hottest / latest ML models and techniques. The practical implications of effectively deploying models into production are thus often overlooked. In fact, very few practitioners talk about it at all in public so there is little information available. Most of the know-how for deploying ML models into production remains internal to the companies who implement it. My goal with this talk is to distill a practical set of techniques for reducing ML model deployment costs to bring it to the forefront of the community.

George is a Machine Learning Engineer with expertise in bringing Machine Learning technologies to production at scale. In the past, he worked at Indus.ai (acquired by Procore Technologies) designing and building a Machine Learning System for applying Computer Vision to construction analytics. He currently works at Altair Engineering where he’s working on building an open, extensible, scalable, cloud-agnostic MLOps platform to make taking ML to production faster and easier.