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MLOps DataOps is a set of tools and principles to support the machine learning project lifecycledelivery of trusted, high-quality data. Below is an overview of the current discussion topics within the MLOps DataOps 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
- DataOps Space and get the community exposed to how these MLOps
- DataOps tools work together and where to use in the pipeline (with pros and cons).
- Exposure to industrial approaches for dataset metadata management, governance, and automation of flow.
- Understand usage of MLOps
- DataOps 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
- Exposure to tools and technologies that can help control the usage of data and securely access it across the enterprise in a cloud native platform.
- Provide an opportunity for committee members to perform research in the MLOps
- DataOps space.
- Educate the community about new developments in the MLOps
- DataOps space.
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Meeting Content (minutes / recording / slides / other):
MLOps Committee Formation Proposal