MLOps-Native Data Governance for Generative AI Pipelines
summary
Claim: embedding a Training Data Declarations (TDD) framework into CI/CD cuts compliance review time by 87% while keeping 100% regulatory adherence. TDD uses a four-tier classification plus a standard metadata schema, real-time risk assessment, audit trails and automated license detection. Implementations tie governance to containers and Kubernetes, and use AWS (S3, Lambda, SageMaker), Azure blobs/functions/ML, GCP storage/functions/Vertex AI and Snowflake. A short Python snippet scans repos and flags risky licenses before deployment.
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chapters (14)
- 0:00Introduction to AI Ethics and ML Ops
- 0:27Speaker Background and Expertise
- 0:58Governance Challenges in AI
- 1:31Introducing the TDED Framework
- 1:59Features of the TDED Framework
- 2:35Implementation Across Platforms
- 3:28Addressing Licensing and Compliance
- 3:58Practical Takeaways and Benefits
- 4:54Deep Dive into TDD in Practice
- 6:52Case Study: Financial Services
- 7:24Cross-Cloud Implementation
- 8:00Python Snippet for License Detection
- 8:24TDD in the ML Ops Pipeline
- 8:58Conclusion and Final Thoughts
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