nannyml
model-validation-toolkit
| nannyml | model-validation-toolkit | |
|---|---|---|
| 7 | 2 | |
| 1,756 | 29 | |
| 1.0% | - | |
| 8.6 | 7.2 | |
| 4 days ago | 5 months ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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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
model-validation-toolkit
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[P] Deepchecks: an open-source tool for high standards validations for ML models and data.
Looks very cool, thanks for sharing! I see someone else already mentioned great_expectations -- in addition, here's another library it might be interesting to integrate with: https://github.com/FINRAOS/model-validation-toolkit
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New open source project: Model Validation Toolkit [P]
Please feel free to reach out to me here, on Gitter, or via Github issues for questions and comments!
What are some alternatives?
evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
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.
cuttle-cli - Cuttle automates the transformation of your Python notebook into deployment-ready projects (API, ML pipeline, or just a Python script)
bodywork - ML pipeline orchestration and model deployments on Kubernetes.
deep-significance - Enabling easy statistical significance testing for deep neural networks.
deeplake - Database for AI. Store Vectors, Images, Texts, Videos, etc. Use with LLMs/LangChain. Store, query, version, & visualize any AI data. Stream data in real-time to PyTorch/TensorFlow. https://activeloop.ai
barfi - Python Flow Based Programming environment that provides a graphical programming environment.
popmon - Monitor the stability of a Pandas or Spark dataframe ⚙︎
ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
eurybia - ⚓ Eurybia monitors model drift over time and securizes model deployment with data validation
great_expectations - Always know what to expect from your data.