Mastering DataOps for Machine Learning
As teams look to productionize their ML efforts, versioning, tagging, and labeling the data becomes even more difficult.
The challenge for MLOps and DataOps teams will be to operationalize their data to better meet the needs of their end-users.
Join us as we cover:
- What is DataOps, and how are teams looking to scale their AI/ML workflows
- The importance of data preparation (versioning and labeling)
- How teams can automate these workflows into robust pipelines
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