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📚 Source Citation and Documenting Data Origins

Understanding where data comes from — and being able to prove it — is critical for trustworthy, ethical, and legally compliant AI systems. This is often referred to as data provenance, and includes techniques like source citation, data lineage tracking, and cataloging.

✅ 1️⃣ Data Lineage​

What it is:

  • Lineage is the record of the complete journey of your data and models — from raw data to features, to training, to deployment.
  • It helps answer:
    “Which dataset trained this model?”
    “Which code and hyperparameters were used?”
    “What container image ran this experiment?”

How it’s done in SageMaker:

  • SageMaker ML Lineage Tracking automatically builds a graphical map of your ML workflow.
    • It tracks processing jobs, training jobs, batch transform jobs, trial components, experiments, and their connections.
    • You can query lineage data, e.g., find all models using a specific dataset.

Why it matters:

  • Provides clear traceability for audits and reproducibility.
  • Supports governance by showing every step in your workflow.
  • Prevents “black box” models by recording exactly how a model was built.

✅ 2️⃣ Data Cataloging​

What it is:

  • Cataloging means organizing and versioning all ML artifacts: datasets, code, container images, models, features, and more.

How it’s done in SageMaker & AWS:

  • Datasets: Stored in Amazon S3, partitioned with prefixes to uniquely identify versions.
  • Code: Managed in repositories like GitHub or AWS CodeCommit, which automatically keep versions of your training and inference scripts.
  • Container Images: Stored in Amazon ECR, uniquely identified by IDs and tags.
  • Features: Managed with Amazon SageMaker Feature Store, which acts as a centralized catalog for feature definitions and metadata. It enables easy discovery and reuse of features across projects.

Why it matters:

  • Reduces duplication of effort.
  • Speeds up experiments by making reusable assets easy to find.
  • Improves collaboration among teams.

✅ 3️⃣ SageMaker Model Cards​

What they are:

  • Model Cards are structured documents that record detailed information about a model — from development to deployment.

What they contain:

  • Intended uses.
  • Risk ratings.
  • Training details.
  • Evaluation results.
  • Other metadata important for risk managers, data scientists, and stakeholders.

Benefits:

  • Provide an immutable, shareable record for governance and audits.
  • Can be exported as PDFs to share with regulators, partners, or internal teams.
  • Support transparency and responsible AI by clearly communicating what a model does, how it was built, and its limitations.

✅ Summary​

Together, data lineage, cataloging, and Model Cards form a strong source citation framework in AWS. They ensure that you:

  • Always know where your data came from.
  • Can prove how your models were built and evaluated.
  • Can reproduce results confidently.
  • Comply with governance requirements and manage risks responsibly.