whitebox
deepchecks
| whitebox | deepchecks | |
|---|---|---|
| 12 | 15 | |
| 181 | 3,380 | |
| 0.6% | 2.5% | |
| 2.8 | 8.2 | |
| 10 months ago | 6 days ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 or later |
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.
whitebox
- [P] We made an open source ML / data monitoring platform
-
A machine learning monitoring platform
Check it out on GitHub: https://github.com/squaredev-io/whitebox And, read the docs here: https://squaredev-io.github.io/whitebox/
- A new ML Monitoring platform. Stars help a lot 🤟
- I'm doing some work on the SDK of Whitebox, a new ML monitoring platform. First time building an SQD for mass consumption in python so feedback is essential. Here is the PR link.
- Hello, I am creating an open source ML monitoring platform. It's still early work and feedback is much appreciated. There is also a planned MLFlow integration. What do you think?
- We just open-sourced Whitebox for machine learning models monitoring!!!
- We just open-sourced Whitebox for machine learning models monitoring....
- We just open-sourced Whitebox for AI models monitoring....
deepchecks
-
Detect, Defend, Prevail: Payments Fraud Detection using ML & Deepchecks
Also if you have any confusion related to it. You can directly go to their discussion section in github :
- Deepchecks: Open-source ML testing and validation library
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Deepchecks' New Open Source is on Product Hunt, and Needs Your Help
GitHub for Deepchecks: https://github.com/deepchecks/deepchecks
- [D] DL Practitioners, Do You Use Layer Visualization Tools s.a GradCam in Your Process?
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Data Validation tools
I use DeepChecks for my continuous training pipelines. You can check out the Data Integrity Checks.
- Deepchecks
- deepchecks: Test Suites for Validating ML Models & Data. Deepchecks is a Python package for comprehensively validating your machine learning models and data with minimal effort.
- QA help comes in many forms: Sometimes, from your heavily funded competitor
- Deepchecks: An open-source tool for testing machine learning models and data
-
Test suites for machine learning models in Python (New OSS package)
And if you liked the project, we'll be delighted to count you as one of our stargazers at https://github.com/deepchecks/deepchecks/stargazers!
What are some alternatives?
great_expectations - Always know what to expect from your data.
evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
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.
feast - The Open Source Feature Store for Machine Learning
postgresml - The GPU-powered AI application database. Get your app to market faster using the simplicity of SQL and the latest NLP, ML + LLM models.
giskard - 🐢 Open-Source Evaluation & Testing framework for LLMs and ML models
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]
polyaxon - MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
cleverhans - An adversarial example library for constructing attacks, building defenses, and benchmarking both
shapash - 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
explainerdashboard - Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.