From the course: Data Versioning, Lineage, and Quality Monitoring for AI
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Summary and further study
From the course: Data Versioning, Lineage, and Quality Monitoring for AI
Summary and further study
- [Instructor] This brings us to the very end of this course on data versioning, lineage, and quality monitoring for AI. Here is a quick overview of what we covered in this course. We started off by discussing the importance of data and model versioning, and we discussed tools, techniques, and use cases of versioning. We also performed hands-on implementation of data versioning using DVC, or data version control. We then moved on to discussing data lineage tracking, which involved visualizing the flow or movement of data from its origin to its final destination, along with all the transformations along the way. We got some hands-on experience here viewing how Microsoft Fabric tracks data lineage. And, finally, we moved on to quality monitoring for data and models. We discussed potential issues that could arise with data and models in the AI workflow, and what you should be tracking and monitoring to mitigate these issues. If you're interested in AI systems and workflows, here are some…
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