Data Version Control Blog

Insights and updates from the DVC team. Explore best practices in data versioning, machine learning workflows, and model management. Stay informed with our latest news, tutorials, and community highlights.

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

September 22, 2026

17 minutes read

Tutorial: Scalable and Distributed ML Workflows with DVC and Ray on AWS (Part 2)

Need to setup DVC to work with Ray Cluster on AWS? This tutorial has you covered!

Mikhail Rozhkov

March 13, 2024

19 minutes read

Tutorial: Scalable and Distributed ML Workflows with DVC and Ray (Part 1)

This tutorial introduces you to integrating DVC (Data Version Control) with Ray, turning them into your go-to toolkit for creating automated, scalable, and distributed ML pipelines.

Mikhail Rozhkov

March 12, 2024

20 minutes read

Running DVC on a SLURM cluster

Learn how Exscientia uses DVC experiments on a cloud-deployed SLURM cluster to scale their ML experimentation.

Dom Miketa

March 11, 2024

11 minutes read

Tutorial: Automate Data Validation and Model Monitoring Pipelines with DVC and Evidently

Ensuring your machine learning models remain precise and efficient as time progresses, and verifying that your data consistently reflects the real-world scenario.

Mikhail Rozhkov

January 19, 2024

14 minutes read