AugLy
evidently
| AugLy | evidently | |
|---|---|---|
| 14 | 14 | |
| 4,969 | 5,453 | |
| 0.2% | 2.5% | |
| 5.7 | 9.7 | |
| 15 days ago | 7 days ago | |
| Python | Jupyter Notebook | |
| GNU General Public License v3.0 or later | 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.
AugLy
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Meta's A.I. exodus: Top talent quits as lab tries to keep pace with rivals
Their recent effort to generate training data for spotting stuff that includes unsanctioned narratives comes to mind. https://github.com/facebookresearch/AugLy
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Next steps for after classification
Data augmentation is usually helpful: https://github.com/facebookresearch/AugLy
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The hand-picked selection of the best Python libraries released in 2021
AugLy.
- Prefer volume or quality for BERT-based Text classification model
- Augly - An augmentation library for audio, image, video, and text from facebook
- [D] What's the best method to generate synthetic data for an image with text? Small dataset
- AugLy is opensourse now.
- Facebook is open-sourcing AugLy, a library that uses data augmentations to evaluate and improve ML models
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Integration test: Complexity of privacy-preserving bird call bio-sensor for distributed ecological monitoring?
Some of the technologies which could be integrated include differential privacy, distributed online machine learning, misinformation resilience and multi-party computation, all within the context of smart contracts and bioinformatics.
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[N] Facebook AI Open Sources AugLy: A New Python Library For Data Augmentation To Develop Robust Machine Learning Models
Facebook Blog: https://ai.facebook.com/blog/augly-a-new-data-augmentation-library-to-help-build-more-robust-ai-models/
evidently
- Evidently: Open-source ML observability platform
- Evidently: An open-source ML and LLM observability framework
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10 Open Source MLOps Projects You Didn’t Know About
Evidently Evidently is an open source monitoring tool built by Evidently AI to help data scientists identify drifts in data, label, model performance changes, and run custom tests. Evidently works with tabular and textual data, including embeddings. In short, it is a tool to evaluate, test, and monitor machine learning models in production.
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20 examples of LLM-powered applications in the real world
The database is maintained by the team behind Evidently, an open-source tool for LLM and ML evaluation and observability. Give us a star on GitHub to support the project!
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[P] Free open-source ML observability course: starts October 16 🚀
Hi everyone, I’m one of the creators of Evidently, an open-source (Apache 2.0) tool for production ML monitoring. We’ve just launched a free open course on ML observability that I wanted to share with the community.
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Free Open-source ML observability course
Evidently itself is an open-source ML monitoring tool with 3m+ downloads so it's fairly popular https://github.com/evidentlyai/evidently. The course will show it but also other OSS tools like Mlflow and Grafana.
Disclaimer: I am one of the people working on Evidently.
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Batch ML deployment and monitoring blueprint using open-source
Repo:https://github.com/evidentlyai/evidently/tree/main/examples/integrations/postgres_grafana_batch_monitoring
- Looking for recommendations to monitor / detect data drifts over time
- evidently: Evaluate and monitor ML models from validation to production
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State of the Art data drift libraries on Python?
Thank you for your answer. I'm trying it today and the the other libraries mentioned + https://github.com/evidentlyai/evidently
What are some alternatives?
imgaug - Image augmentation for machine learning experiments.
great_expectations - Always know what to expect from your data.
PySyft - Perform data science on data that remains in someone else's server
seldon-core - An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
skweak - skweak: A software toolkit for weak supervision applied to NLP tasks
whylogs - An open-source data logging library for machine learning models and data pipelines. 📚 Provides visibility into data quality & model performance over time. 🛡️ Supports privacy-preserving data collection, ensuring safety & robustness. 📈
speechbrain - A PyTorch-based Speech Toolkit
MLflow - Open source platform for the machine learning lifecycle
river - 🌊 Online machine learning in Python
ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
BlenderProc - A procedural Blender pipeline for photorealistic training image generation
deepchecks - Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.