nannyml
cuttle-cli
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| nannyml | cuttle-cli | |
|---|---|---|
| 7 | 9 | |
| 1,754 | 48 | |
| 2.2% | - | |
| 8.8 | 0.0 | |
| 5 days ago | over 2 years ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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nannyml
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Introduction to NannyML: Model Evaluation without labels
In order to try to solve this issue, NannyML was created. NannyML is an open-source Python library designed in order to make it easy to monitor drift in the distributions of our model input variables and estimate our model performance (even without labels!) thanks to the Confidence-Based Performance Estimation algorithm they developed. But first of all, why do models need to be monitored and why their performance might vary over time?
- Detecting silent model failure. NannyML estimates performance for regression and classification models using tabular data. It alerts you when and why it changed. It is the only open-source library capable of fully capturing the impact of data drift on performance.
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[D] Data drift is not a good indicator of model performance degradation
But I may have it haha. What we propose in the blog post instead of relying solely on data drift is using performance estimation methods (eg: https://github.com/NannyML) with them you can estimate the performance of the ml model without having access to ground truth.
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[HIRING][Full Time, Part Time, Temporary, Internship, Freelance] Data Science Intern (Remote)
Description NannyML - creators of an Open Source Python library, are looking for multiple Data Science interns to help across research, prototyping, and product. Github: https://github.com/NannyML/nannyml About Us NannyML is an Open Source Python lib …
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What do you think about Detecting Silent ML Failure with an Open Source Python library?
If you think this could add value to your daily life, check it out here: https://github.com/NannyML/nannyml.
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Can I estimate the impact of data drift on performance?
I found it implemented here: https://github.com/NannyML/nannyml
- Show HN: OSS Python library for detecting silent ML model failure
cuttle-cli
- Magically generate an API project from your Python notebook without writing extra code
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Convert a Python Emotion Recognition Notebook into an API without extra code
Cuttle Website: cuttle.it Github: Emotion recognition API source Cuttle Source: Cuttle CLI Source
- Magically generate an API project from your Python notebook without writing extra code.
What are some alternatives?
evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
fastapi-template - Completely Scalable FastAPI based template for Machine Learning, Deep Learning and any other software project which wants to use Fast API as an API framework.
deep-significance - Enabling easy statistical significance testing for deep neural networks.
Emotion-Recognition-API
barfi - Python Flow Based Programming environment that provides a graphical programming environment.
go-unsplash - Go Client for the Unsplash API
ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
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]
eurybia - ⚓ Eurybia monitors model drift over time and securizes model deployment with data validation
argilla - Argilla is a collaboration platform for AI engineers and domain experts that require high-quality outputs, full data ownership, and overall efficiency.
cyclops - Toolkit for health AI implementation
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.