Mikhail Rozhkov discusses the landscape of features and use cases of DVC in ML engineering and MLOps at DSC Adria Conference 2023.
- Get started with versioning data, artifacts, and models with Data Version Control (DVC);
- Automated and reproducible pipelines with DVC;
- Experiment management and metrics tracking with DVC;
- DVC in MLOps practices
Find out more about DSC Adria here: [ Ссылка ]
*Try out the DVC Extension for VS Code here:* [ Ссылка ]
To learn more about Iterative's open-source and SaaS tools please visit:
🧑🏽💻 *Our free online course:* [ Ссылка ]
✍🏼 *Our docs:* [ Ссылка ] (Data Version Control, Pipelines, Experiments)
[ Ссылка ] (CI/CD for Machine Learning)
[ Ссылка ] (Package and Serve your models)
[ Ссылка ] (Team Collaboration, Experiments, Model Registry)
*Join the Community on our Discord server:* [ Ссылка ]
#dvc #machinelearning #datascience #generativeai
DVC in Machine Learning Engineering and MLOps Practices
Теги
data version controlautomated machine learning pipelinemachine learning artifact versioningDVCMLOpsGitOpsModelOpsreproducible data sciencereproducible machine learningmachine learning experiment versioningmachine learning metrics trackingbest mlops practicesreproducible machine learning modelsreproducible dataml engineeringbest practices for machine learning engineermachine learning model monitoringEvidently AI
