MLOps is a set of tools and principles to support the machine learning project lifecycle. Below is an overview of the current discussion topics within the MLOps Committee. Further updates will follow as the committee work develops.
- Exposure to industrial approaches for managing Machine Learning models in production and create template architecture for managing Machine Learning project lifecycle
- Identify Projects and tools in MLOps Space and get the community exposed to how these MLOps tools work together and where to use in the pipeline (with pros and cons)
- Understand usage of MLOps tools and practices through industrial use cases (by domain). Identify gaps in the use case implementation and discuss solutions to bridge the gap.
- Take a data-centric Approach in managing ML model performance in production. Learn tools and best practices on a data-centric approach
- Provide an opportunity for committee members to perform research in the MLOps space
- Educate the community about new developments in the MLOps space.
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