A JSON-based format for working with machine learning data, with a focus on data interoperability.
-
Updated
Jul 19, 2022
A JSON-based format for working with machine learning data, with a focus on data interoperability.
Learning data and model versioning with ClearML while cleaning and modeling happiness by country with a Kaggle dataset
In this course navigates through the LLMOps pipeline, enabling you to preprocess training data for supervised fine-tuning and deploy custom Large Language Models (LLMs).
Python Data as Code core implementation
Obtain data versioning tag using ML models
DVC + MLflow for data monitoring and ML lifecycle management
Git-like data versioning.
practice about data_version_control(DVC)
Testing and implementations with ClearML
Advanced Machine Learning Regression: Predicting Car Prices
Automatic data change tracking for TypeORM
Project with tabular data versioned with Artifacts.
The provided demo project demonstrates the practical implementation and advantages of using DVC. It showcases how DVC simplifies data versioning and model versioning while working in tandem with Git to create a cohesive version control system tailored for data science projects.
A demonstration of how DVC and MLFlow can be used in the task of data relabeling
Automatic data change tracking for Prisma
Verta ai ModelDB on AWS Cloud with integration into Amazon SageMaker for ML training data versioning and experiment tracking
following best practices to productionize an ML project
Python framework for artificial text detection: NLP approaches to compare natural text against generated by neural networks.
Repository for evaluating the different approaches to data versioning
Articles, tutorials, and tools about creating scalable and sustainable ML/DL systems.
Add a description, image, and links to the data-versioning topic page so that developers can more easily learn about it.
To associate your repository with the data-versioning topic, visit your repo's landing page and select "manage topics."