awesome-open-data-centric-ai
spotlight
| awesome-open-data-centric-ai | spotlight | |
|---|---|---|
| 1 | 24 | |
| 678 | 1,014 | |
| - | - | |
| 5.8 | 9.8 | |
| 6 months ago | about 2 months ago | |
| TypeScript | ||
| Creative Commons Attribution 4.0 | MIT License |
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awesome-open-data-centric-ai
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[P] We are building a curated list of open source tooling for data-centric AI workflows, looking for contributions.
Here is the link to the Github repo: https://github.com/Renumics/awesome-open-data-centric-ai Do you think there are tools missing? Please let me know or feel free to submit a pull request.
spotlight
- Renumics/spotlight: Interactively explore unstructured datasets from dataframes
- Open source data analysis software for vehicle data/telemetry - feedback and contributions welcome
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OSS project: Interactively explore 🤗 datasets with one line of code
If run into trouble feel free to open an issue on Github: https://github.com/Renumics/spotlight
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Show HN: Interactively explore your Hugging Face dataset with one line of code
Hey HN,
when building ML systems for industrial AI, we have learned that data inspection is critical during the ML development process. We are also big fans of the Hugging Face ecosystem.
That is why we built an integration to our data exploration tool Spotlight that allows you to interactively explore Hugging Face datasets with one line of code.
Spotlight lets you leverage model results such as predictions and embeddings to gain a deeper understanding in data segments and model failure modes.
Currently, many many NLP, CV, Audio and multimodal datasets are supported both locally and on the hub.
You can find more info on the Hugging Face blog: https://huggingface.co/blog/scalable-data-inspection
Link to repo: https://github.com/Renumics/spotlight
Happy to hear your feedback!
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Hacktoberfest Machine Learning Projects for JS/TS Developers 🎃
Renumics Spotlight is a powerful tool for intuitively exploring unstructured datasets directly from dataframes. It’s designed to simplify data comprehension, allowing users to create interactive visualizations and leverage data enrichments like embeddings, predictions, and uncertainties to identify critical data clusters.
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[P] Quick fine-tuning image classification models from Bing image search
I've been working on our interactive dataset explorer for machine learning: renumics-spotlight. Recently, I set up an image classification example using it. I utilized Bing image search to create a fully functional example for custom-defined classes.
- Unstructured Data? No Problem. Spotlight Makes Exploration a Breeze!
- Show HN: Interactively explore unstructured datasets from your dataframe
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[P] Interactively explore unstructured datasets from your dataframe (OSS project)
That is why we developed Spotlight: https://github.com/Renumics/spotlight
What are some alternatives?
internet-explorer - Internet Explorer explores the web in a self-supervised manner to progressively find relevant examples that improve performance on a desired target dataset.
Low-Cost-Mocap - Low cost motion capture system for room scale tracking
Encord Active - Open source active learning toolkit to find failure modes in your computer vision models, prioritize data to label next, and drive data curation to improve model performance.
cml - ♾️ CML - Continuous Machine Learning | CI/CD for ML
WhereIsAI - AI company, product, and tool collection.
inclusive-code-reviews-browser - A chrome web extension for improving code reviews on Github or Azure DevOps
awesome-synthetic-data - 📖 A curated list of resources dedicated to synthetic data
BeatBridge - A Music Player with a Clustering based Recommendation Engine utilizing Spotify API
Awesome-Learning-with-Label-Noise - A curated list of resources for Learning with Noisy Labels
racing-data-converter - Converts data into popular motorsport analysis formats
cleanlab - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
sliceguard - A library for detecting problematic data segments in structured and unstructured data with few lines of code.