mlops2020: Workshop on MLOps Systems Austin Convention Center Austin, TX, United States, March 1-4, 2020 |
Conference website | https://mlops-systems.github.io |
Submission link | https://easychair.org/conferences/?conf=mlops2020 |
Abstract registration deadline | January 8, 2020 |
Submission deadline | January 15, 2020 |
Due to the complexity in putting ML into production, the actual machine learning capability is a small part of a complex system and its lifecycle. This new evolving field is known as MLOps. Informally MLOps typically refers to the collaboration between data scientists and operations engineers (e.g. SRE) to manage the lifecycle of ML within an organization. This space is new and has yet to be explored from a research perspective.
In this workshop we aim to cover research problems in MLOps, including the systems and ML challenges involved in this process. We will also cover the software engineering questions including specification, testing and verification of ML software systems. We will bring together a wide variety of experts from both industry and academia, covering persona ranging from data scientists to machine learning engineers.
Topics that are relevant to this workshop include
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ML model specification
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ML model management
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ML model or Concept drift/change detection
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ML training pipelines specification, verification
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ML model monitoring
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ML model serving techniques and systems
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ML model specification
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ML Experiment tracking and management
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System design for metadata management systems
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Audits, assurance, security and compliance for MLOps.
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Integrating Benchmarking in MLOps systems and orchestration
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ML CI/CD
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Scheduling of ML workflows.
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Cost optimization of ML workflows
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Debugging ML workflows and pipelines.
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Hyperparameter tuning systems
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MLops for Federated and Split Learning