inferencedb
nannyml
Our great sponsors
| inferencedb | nannyml | |
|---|---|---|
| 9 | 7 | |
| 77 | 1,754 | |
| - | 2.2% | |
| 0.0 | 8.8 | |
| almost 2 years ago | 5 days ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
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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.
inferencedb
- [P] InferenceDB - Makes it easy to store predictions of real-time ML models in S3
- InferenceDB: Stream inferences of real-time ML models to S3 using Kafka
- InferenceDB: Stream inferences of real-time ML models in production to any data lake 🚀
- InferenceDB – Stream ML inferences to S3 or any data lake with CRDs
- InferenceDB – Stream predictions from KServe to S3 or any data lakes
- InferenceDB – Stream predictions of real-time ML models to data lakes
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
What are some alternatives?
kfserving - Standardized Serverless ML Inference Platform on Kubernetes [Moved to: https://github.com/kserve/kserve]
evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
cuttle-cli - Cuttle automates the transformation of your Python notebook into deployment-ready projects (API, ML pipeline, or just a Python script)
deep-significance - Enabling easy statistical significance testing for deep neural networks.
barfi - Python Flow Based Programming environment that provides a graphical programming environment.
ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
eurybia - âš“ Eurybia monitors model drift over time and securizes model deployment with data validation
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.
frouros - Frouros: an open-source Python library for drift detection in machine learning systems.
model-validation-toolkit - Model Validation Toolkit is a collection of tools to assist with validating machine learning models prior to deploying them to production and monitoring them after deployment to production.
pytest-visual - A visual testing framework for ML with automated change detection