fides VS dvc

Compare fides vs dvc and see what are their differences.

Scout Monitoring - Free Django app performance insights with Scout Monitoring
Get Scout setup in minutes, and let us sweat the small stuff. A couple lines in settings.py is all you need to start monitoring your apps. Sign up for our free tier today.
www.scoutapm.com
featured
CodeRabbit: AI Code Reviews for Developers
Revolutionize your code reviews with AI. CodeRabbit offers PR summaries, code walkthroughs, 1-click suggestions, and AST-based analysis. Boost productivity and code quality across all major languages with each PR.
coderabbit.ai
featured
fides dvc
3 118
359 13,840
1.1% 1.4%
9.8 9.4
3 days ago 5 days ago
Python Python
Apache License 2.0 Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

fides

Posts with mentions or reviews of fides. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-03-26.

dvc

Posts with mentions or reviews of dvc. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-10-19.
  • Data Version Control
    5 projects | news.ycombinator.com | 19 Oct 2024
  • S3 as a Git remote and LFS server
    10 projects | news.ycombinator.com | 19 Oct 2024
    I haven't heard of dvc, so I had to google it, which took me to: https://dvc.org/

    But I'm still confused as to what is dvc is after a cursory glance at their homepage.

  • serverless-registry: A Docker registry backed by Workers and R2
    11 projects | news.ycombinator.com | 5 Sep 2024
    I’m self-hosting gitea just for their private docker registry. LFS is actually slow for heavy deep learning workflow with millions of small files. I’m using DVC [1] instead.

    [1]: https://dvc.org

  • GitOps ML Experiments, data versioning, model registry
    1 project | news.ycombinator.com | 31 Aug 2024
  • 25 Open Source AI Tools to Cut Your Development Time in Half
    8 projects | dev.to | 11 Jul 2024
    Implementing version control for machine learning projects entails managing both code and the datasets, ML models, performance metrics, and other development-related artifacts. Its purpose is to bring the best practices from software engineering, like version control and reproducibility, to the world of data science and machine learning. DVC enables data scientists and ML engineers to track changes to data and models like Git does for code, making it able to run on top of any Git repository. It enables the management of model experiments.
  • Essential Deep Learning Checklist: Best Practices Unveiled
    20 projects | dev.to | 17 Jun 2024
    Tool: Consider using Data Version Control (DVC) to manage your datasets, models, and their respective versions. DVC integrates with Git, allowing you to handle large data files and model binaries without cluttering your repository. It also makes it easy to version your training datasets and models, ensuring you can always match a model back to its exact training environment.
  • 10 Open Source Tools for Building MLOps Pipelines
    9 projects | dev.to | 6 Jun 2024
    As Git helps you with code versions and the ability to roll back to previous versions for code repositories, DVC has built-in support for tracking your data and model. This helps machine learning teams reproduce the experiments run by your fellows and facilitates collaboration. DVC is based on the principles of Git and is easy to learn since the commands are similar to those of Git. Other benefits of using DVC include:
  • A step-by-step guide to building an MLOps pipeline
    7 projects | dev.to | 4 Jun 2024
    The meta-data and model artifacts from experiment tracking can contain large amounts of data, such as the training model files, data files, metrics and logs, visualizations, configuration files, checkpoints, etc. In cases where the experiment tool doesn't support data storage, an alternative option is to track the training and validation data versions per experiment. They use remote data storage systems such as S3 buckets, MINIO, Google Cloud Storage, etc., or data versioning tools like data version control (DVC) or Git LFS (Large File Storage) to version and persist the data. These options facilitate collaboration but have artifact-model traceability, storage costs, and data privacy implications.
  • AI Strategy Guide: How to Scale AI Across Your Business
    4 projects | dev.to | 11 May 2024
    Level 1 of MLOps is when you've put each lifecycle stage and their intefaces in an automated pipeline. The pipeline could be a python or bash script, or it could be a directed acyclic graph run by some orchestration framework like Airflow, dagster or one of the cloud-provider offerings. AI- or data-specific platforms like MLflow, ClearML and dvc also feature pipeline capabilities.
  • My Favorite DevTools to Build AI/ML Applications!
    9 projects | dev.to | 23 Apr 2024
    Collaboration and version control are crucial in AI/ML development projects due to the iterative nature of model development and the need for reproducibility. GitHub is the leading platform for source code management, allowing teams to collaborate on code, track issues, and manage project milestones. DVC (Data Version Control) complements Git by handling large data files, data sets, and machine learning models that Git can't manage effectively, enabling version control for the data and model files used in AI projects.

What are some alternatives?

When comparing fides and dvc you can also consider the following projects:

awesome-machine-unlearning - Awesome Machine Unlearning (A Survey of Machine Unlearning)

MLflow - Open source platform for the machine learning lifecycle

differential-privacy-library - Diffprivlib: The IBM Differential Privacy Library

lakeFS - lakeFS - Data version control for your data lake | Git for data

fiftyone - Refine high-quality datasets and visual AI models

Activeloop Hub - Data Lake for Deep Learning. Build, manage, query, version, & visualize datasets. Stream data real-time to PyTorch/TensorFlow. https://activeloop.ai [Moved to: https://github.com/activeloopai/deeplake]

fideslang - Open-source description language for privacy to declare data types and data behaviors in your tech stack in order to simplify data privacy globally. Supports GDPR, CCPA, LGPD and ISO 19944.

delta - An open-source storage framework that enables building a Lakehouse architecture with compute engines including Spark, PrestoDB, Flink, Trino, and Hive and APIs

datahub - The Metadata Platform for your Data Stack

ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️

pandas-datareader - Extract data from a wide range of Internet sources into a pandas DataFrame.

aim - Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.

Scout Monitoring - Free Django app performance insights with Scout Monitoring
Get Scout setup in minutes, and let us sweat the small stuff. A couple lines in settings.py is all you need to start monitoring your apps. Sign up for our free tier today.
www.scoutapm.com
featured
CodeRabbit: AI Code Reviews for Developers
Revolutionize your code reviews with AI. CodeRabbit offers PR summaries, code walkthroughs, 1-click suggestions, and AST-based analysis. Boost productivity and code quality across all major languages with each PR.
coderabbit.ai
featured