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End-to-End Lineage with Amazon SageMaker AI, MLflow & DVC

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End-to-end lineage with DVC and Amazon SageMaker AI MLflow apps
AWS Solutions Architects demonstrate how to build an end-to-end MLOps workflow combining DVC for data versioning, Amazon SageMaker AI for scalable training, and SageMaker AI MLflow Apps for experiment tracking and lineage. The post presents two deployable patterns: dataset-level lineage (foundational) and record-level lineage (healthcare compliance with individual record traceability), enabling full reproducibility and audit capabilities for regulated industries.
Jeny De Figueiredo
Jeny De Figueiredo
September 22, 2026
17 minutes read
From Jupyter Notebook to DVC pipeline for reproducible ML experiments
In this guide we will take a Jupyter Notebook and use Papermill to turn it into a simple, one-stage DVC pipeline.
Rob de Wit
Rob de Wit
October 24, 2022
13 minutes read
CML Cloud Runners for Model Training in Bitbucket Pipelines
Use CML from a Bitbucket pipeline to provision an AWS EC2 instance and (re)train a machine learning model.
Rob de Wit
Rob de Wit
September 6, 2022
7 minutes read
August ’22 Community Gems
A roundup of technical Q&A's from the DVC community. This month: explaining DVC versioning mechanism, some tricks with pipelines and CML action, visualizing plots in VS Code extension.
Gema Parreno
Gema Parreno
August 30, 2022
7 minutes read
July ’22 Community Gems
A roundup of technical Q&A's from the DVC community. This month: deploying models MLEM, DVC data and remotes, DVC stages and plots, and more.
Milecia McGregor
Milecia McGregor
July 26, 2022
6 minutes read
June ’22 Community Gems
A roundup of technical Q&A's from the DVC and CML communities. This month: working with the DVC cache, DVC data and remotes, using DVC programmatically, and more.
Milecia McGregor
Milecia McGregor
June 29, 2022
6 minutes read