CollaRE
dvc
CollaRE | dvc | |
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2 | 112 | |
134 | 13,311 | |
- | 1.5% | |
5.0 | 9.6 | |
3 months ago | 4 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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.
CollaRE
dvc
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10 Open Source Tools for Building MLOps Pipelines
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:
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A step-by-step guide to building an MLOps pipeline
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.
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AI Strategy Guide: How to Scale AI Across Your Business
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.
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My Favorite DevTools to Build AI/ML Applications!
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.
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Why bad scientific code beats code following "best practices"
What you’re describing sounds like DVC (at a higher-ish—80%-solution level).
https://dvc.org/
See pachyderm too.
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First 15 Open Source Advent projects
10. DVC by Iterative | Github | tutorial
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
Platforms such as MLflow monitor the development stages of machine learning models. In parallel, Data Version Control (DVC) brings version control system-like functions to the realm of data sets and models.
- ML Experiments Management with Git
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Git Version Controlled Datasets in S3
I was using DVC (https://dvc.org/) for some time to help solve this but it was getting hard to manage the storage connections and I would run into cache issues a lot, but this solves it using git-lfs itself.
- Ask HN: How do your ML teams version datasets and models?
What are some alternatives?
MLflow - Open source platform for the machine learning lifecycle
lakeFS - lakeFS - Data version control for your data lake | Git for data
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]
delta - An open-source storage framework that enables building a Lakehouse architecture with compute engines including Spark, PrestoDB, Flink, Trino, and Hive and APIs
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
aim - Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.
git-submodules - Git Submodule alternative with equivalent features, but easier to use and maintain.
palm-dbt - dbt plugin for Palm CLI
git-lfs - Git extension for versioning large files
guildai - Experiment tracking, ML developer tools
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
quilt - Quilt is a data mesh for connecting people with actionable data