Workshop on Challenges in Deploying and monitoring Machine Learning Systems

CALL FOR PAPERS

SUBMISSION DEADLINE: 17th of June

We invite submissions of full papers on machine learning systems, deployment and testing, including:

•  MLOps for deployed ML systems
• ethics around deploying ML systems; 
• fairness, trust and transparency of ML systems; 
• providing privacy and security on ML Systems;
• useful tools and programming languages for deploying ML systems;
• deploying reinforcement learning in ML systems
• performing continual learning and providing continual delivery in ML systems;
• data challenges for deployed ML systems

All papers submissions will be made in PDF format,  with a limit of four pages, including figures and tables, excluding references and appendices. 

Formatting instructions are provided in the ICML website. The reviewing process will be blind and the workshop allows re-submissions of already published work and double submission.

Submission can be made via the EasyChair system.

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CALL FOR OPEN PROBLEMS

SUBMISSION DEADLINE: 30th of June

We invite you to submit open challenges in deploying and monitoring ML systems which you feel are only partially, or are wholly unsolved at the present time. These problems might be algorithmic, computational, programmatic or hardware-based in nature. However, in all cases they should represent a significant blocker in the continual use of a machine learning system.

High level topics submitted problems might relate to include:

• Updating machine learning systems, or managing the life-cycle of machine learning systems, whether manually or automatically (potentially using machine learning itself) 
• Machine learning systems for resource-limited hardware, e.g. industrial devices or low memory/low power devices 
• Detecting, and responding to changes in the data, e.g. data drift, or adversarial attacks
• Detecting and responding to unexpected events, e.g. oddball or missing data
• Ensuring an ML system is and continues to be compliant with any existing regulations, e.g. with respect to regulations about data privacy
• Measuring the impact of deploying an ML system, e.g. how to measure the carbon-footprint of a deployed ML system

If possible, in addition to describing the problem, you should give examples where your submitted problem has proved a blocker to success deployment or monitoring of an ML system.

After the submission deadline, we will collate all submissions, and publish a register of problems on the workshop’s website.  We will then invite the authors of the submissions representing the most important and challenging problems to participate in a live broadcast panel discussion during the workshop. 

--> SUBMIT VIA EMAIL to: DMMLSys+submission2020@gmail.com <--

Organizing committee

• Alessandra Tosi
• Nathan Korda
• Neil Lawrence

Invited Speakers

• Ernest Mwebaze
• Nevena Lalic
• Leilani Gilpin
• Mohammad Ghavamzadeh

Venue

The workshop will be held as a virtual event, co-located with the 37th International Conference in Machine Learning.

Contact

All questions about submissions should be emailed to atosi@robots.ox.ac.uk

Sponsors

TBA