Machine learning (ML) (and its variants deep learning (DL), reinforcement learning, etc.) is starting to impact every commercial industry. The 2019 USENIX Conference on Operational Machine Learning (OpML '19), dedicated to operational machine learning and its variants, will focus on the final stage of deploying and managing ML into production and the subsequent continuous lifecycle of ML in production.
Who Should Attend?
OpML '19 will bring together machine learning and systems researchers and practitioners such as data scientists, data engineers, sysadmins, and DevOps specialists to develop and bring to practice impactful research advances and cutting edge solutions to the pervasive problem of ML continuous lifecycle management in production.
What You Can Expect to Get from OpML
- Machine Learning Researchers: Understand the practical challenges, learn about advances in ML explainability, diagnostics, etc. Gain exposure to latest hardware advances.
- Systems Researchers: Share innovative techniques for managing ML/DL in production, and learn from the experiences of real-life production challenges.
- Data Scientists / Data Engineers: Gain exposure to best practices in production ML in the contexts of compliance, risk management, and legal requirements.
- System/IT Administrators and DevOps Specialists: Learn best practices for deploying ML algorithms. Explore the latest software and hardware advances available for production ML lifecycle management.
OpML '19 will be held Monday, May 20, 2019, at the Hyatt Regency Santa Clara in Santa Clara, CA, USA. The Call for Participation will be available in December 2018, and the submission deadline will be Friday, January 11, 2019.
- Use of new and emerging analytic engines (e.g., Spark, TensorFlow) in production
- Use of new and emerging hardware acceleration for improving the ML/DL performance
- Systems for orchestrating, diagnosing, monitoring and managing ML in production
- Applying existing DevOps and SDLC tools to the ML operational lifecycle
- Diagnostics of ML algorithms operating on live data (e.g., drift detection)
- New model introduction into production (e.g., staging, A/B test)
- Governance of ML models and deployment processes, model risk management
- New visualizations to accommodate diverse users (engineers, data scientists, ops, etc.)
- Experiences with advanced techniques (such as Reinforcement Learning) in production.
- Technical advances for addressing ML regulatory requirements (e.g., GDPR)
- Privacy and security challenges in production ML
- Bringing research on Explainable ML into production use
- Experiences with bringing ML techniques to production and scaling ML in production
- Industry-specific best practices for ML production (e.g., edge computing/IoT, healthcare)