Challenges in Deploying and monitoring Machine Learning Systems

Until recently, many industrial Machine Learning applications have been the remit of consulting academics, data scientists within larger companies, and a number of dedicated Machine Learning research labs within a few of the world’s most innovative tech companies. Over the last few years we have seen the dramatic rise of companies dedicated to providing Machine Learning software-as-a-service tools, with the aim of democratizing access to the benefits of Machine Learning. All these efforts have revealed major hurdles to ensuring the continual delivery of good performance from deployed Machine Learning systems. These hurdles range from challenges in MLOps, to fundamental problems with deploying certain algorithms, to solving the legal issues surrounding the ethics involved in letting algorithms make decisions for your business.

This workshop will invite papers related to the challenges in deploying and monitoring ML systems. It will encourage submission on subjects related to: MLOps for deployed ML systems; the ethics around deploying ML systems; useful tools and programming languages for deploying ML systems; specific challenges relating to deploying reinforcement learning in ML systems and performing continual learning and providing continual delivery in ML systems;
and finally data challenges for deployed ML systems.

We will also invite the submission of open problems and encourage the discussion (through two live panels) on topics related to the areas of: "Deploying machine learning applications in the legal system" and "Deploying machine learning on devices or constrained hardware".

These subjects represent a wealth of topical and high-impact issues for the community to work on.