Overview

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.

Mail List

Please self subscribe to the mail list here at https://lists.lfaidata.foundation/g/mlops-committee

Or email mlops-committee@lists.lfaidata.foundation for more information

Meetings

Insert meeting cadence, day of week, time, timezone 

Subscribe to calendar via Groups.io mailing list: https://lists.lfaidata.foundation/g/mlops-committee/calendar 

View group calendar: MLOps Committee - Community Meetings & Calendar

Meeting Content (minutes / recording / slides / other):

References

• MLOps Committee Formation Proposal