---
title: "End-to-end lineage with DVC and Amazon SageMaker AI MLflow apps"
slug: "end-to-end-lineage-with-dvc-and-amazon-sagemaker-ai-mlflow-apps"
date: 2026-04-21
category: tech-pub
tags: [amazon]
language: en
sources_count: 1
featured: false
publisher: AInauten News
url: https://news.ainauten.com/en/story/end-to-end-lineage-with-dvc-and-amazon-sagemaker-ai-mlflow-apps
---

# End-to-end lineage with DVC and Amazon SageMaker AI MLflow apps

**Published**: 2026-04-21 | **Category**: tech-pub | **Sources**: 1

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## TL;DR

In this post, we show how to combine DVC (Data Version Control), Amazon SageMaker AI, and Amazon SageMaker AI MLflow Apps to build end-to-end ML model lineage.

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## Summary

In this post, we show how to combine DVC (Data Version Control), Amazon SageMaker AI, and Amazon SageMaker AI MLflow Apps to build end-to-end ML model lineage. We walk through two deployable patterns — dataset-level lineage and record-level lineage — that you can run in your own AWS account using the companion notebooks.

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## Why it matters

In this post, we show how to combine DVC (Data Version Control), Amazon SageMaker AI, and Amazon SageMaker AI MLflow Apps to build end-to-end ML model lineage.

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## Key Points

- In this post, we show how to combine DVC (Data Version Control), Amazon SageMaker AI, and Amazon SageMaker AI MLflow Apps to build end-to-end ML model lineage.
- We walk through two deployable patterns — dataset-level lineage and record-level lineage — that you can run in your own AWS account using the companion notebooks.

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## Nauti's Take

End-to-end ML lineage is critical and often overlooked – DVC and SageMaker MLflow together make it concrete and reproducible. Teams shipping models without lineage tracking risk serious gaps during audits or incident investigations. If you run ML in AWS, this two-pattern approach is worth evaluating for your stack.

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## FAQ

**Q:** What is End-to-end lineage with DVC and Amazon SageMaker AI MLflow apps about?

**A:** In this post, we show how to combine DVC (Data Version Control), Amazon SageMaker AI, and Amazon SageMaker AI MLflow Apps to build end-to-end ML model lineage.

**Q:** Why does it matter?

**A:** In this post, we show how to combine DVC (Data Version Control), Amazon SageMaker AI, and Amazon SageMaker AI MLflow Apps to build end-to-end ML model lineage.

**Q:** What are the key takeaways?

**A:** In this post, we show how to combine DVC (Data Version Control), Amazon SageMaker AI, and Amazon SageMaker AI MLflow Apps to build end-to-end ML model lineage.. We walk through two deployable patterns — dataset-level lineage and record-level lineage — that you can run in your own AWS account using the companion notebooks.

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## Related Topics

- [amazon](https://news.ainauten.com/en/tag/amazon)

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## Sources

- [End-to-end lineage with DVC and Amazon SageMaker AI MLflow apps](https://aws.amazon.com/blogs/machine-learning/end-to-end-lineage-with-dvc-and-amazon-sagemaker-ai-mlflow-apps/) - AWS Machine Learning Blog

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## About This Article

This article is a synthesis of 1 sources, curated and summarized by AInauten News. We aggregate AI news from trusted sources and provide bilingual (German/English) coverage.

**Publisher**: [AInauten](https://www.ainauten.com) | **Site**: [news.ainauten.com](https://news.ainauten.com)

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*Last Updated: 2026-04-21*