aim
dvc
| aim | dvc | |
|---|---|---|
| 70 | 110 | |
| 4,865 | 13,246 | |
| 3.2% | 1.6% | |
| 7.9 | 9.6 | |
| 3 days ago | 5 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.
aim
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aim VS cascade - a user suggested alternative
2 projects | 5 Dec 2023
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End-to-end observability for LlamaIndex environment
LlamaIndex Observer is one of the logging apps built in AimOS (aimstack.io).
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Data Registry suggestions for ML projects
I've been working with Aim for a while, and it's been solid. What stands out for me is its open-source nature. https://aimstack.io/
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Building and debugging LLMs with Aim: self-hosted and open-source AI metadata tracking tool
If you haven't yet, drop a star to support open-source project! ⭐️ https://github.com/aimhubio/aim
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Any tools that offer In-depth tracking of model runtime performance?
Here is the GitHub repository: https://github.com/aimhubio/aim
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Using MLflow(Machine Learning experimentation tracking tool) in Kaggle notebooks with the help of DagsHub
You can also check out Aim, which has an integration with MLflow, called aimlflow.
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Visualize metadata with Aim on Hugging Face Spaces and seamlessly share training results with anyone
Hope you enjoyed reading and thanks for your time! Feel free to share your thoughts, would love to read them. Support Aim by dropping a star on GitHub: https://github.com/aimhubio/aim
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Effortless image tracking and analysis for 3D segmentation task with Aim
Aim: An easy-to-use & supercharged open-source AI metadata tracker aimstack.io
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Evaluate Different Vector Databases
Seems useful: https://github.com/aimhubio/aim
- Metadata visualization via Aim Explorers
dvc
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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?
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Exploring MLOps Tools and Frameworks: Enhancing Machine Learning Operations
DVC (Data Version Control):
- Evaluate and Track Your LLM Experiments: Introducing TruLens for LLMs
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[D] Is there a tool to keep track of my ML experiments?
I have been using DVC and MLflow since then DVC had only data tracking and MLflow only model tracking. I can say both are awesome now and maybe the only factor I would like to mention is that IMO, MLflow is a bit harder to learn while DVC is just a git practically.
What are some alternatives?
tensorboard - TensorFlow's Visualization Toolkit
MLflow - Open source platform for the machine learning lifecycle
guildai - Experiment tracking, ML developer tools
lakeFS - lakeFS - Data version control for your data lake | Git for data
wandb - 🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.
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]
Sacred - Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
delta - An open-source storage framework that enables building a Lakehouse architecture with compute engines including Spark, PrestoDB, Flink, Trino, and Hive and APIs
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
git-submodules - Git Submodule alternative with equivalent features, but easier to use and maintain.