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

Artificial Intelligence End-to-end lineage with DVC and Amazon SageMaker AI MLflow apps Production machine learning (ML) teams struggle to trace the full lineage of a model through the data and the code that trained it, the exact dataset version it consumed, and the experiment metrics that justified its deployment. Without this traceability, questions like “which data trained the model currently in production?” or “can we reproduce the model we deployed six months ago?” become multi-day investigations through scattered logs, notebooks, and Amazon Simple Storage Service (Amazon S3) buckets. This gap is especially acute in regulated industries. For example, healthcare, financial services, autonomous vehicles, where audit requirements demand that you link deployed models to their precise training data, and where individual records might need to be excluded from future training on request. In this post, we show how to combine three…