|Table of Contents|
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.
Or email email@example.com for more information
|Saishruthi Swaminathan||North America||IBMfirstname.lastname@example.org|
|Adrian Gonzalez Sanchez||Canada|
Projects in Scope
Insert meeting cadence, day of week, time, timezone
View group calendar: MLOps DataOps Committee - Community Meetings & Calendar
Meeting Content (minutes / recording / slides / other):
|March 18, 2022|
Data Governance for data-driven and AI-enabled companies
|February 10, 2022|
Egeria and OpenLineage Integration - Recording Link
|Oct 14, 2021|
MLOps Committee Formation Proposal