cascade
deepchecks
| cascade | deepchecks | |
|---|---|---|
| 9 | 15 | |
| 16 | 3,400 | |
| - | 3.1% | |
| 9.3 | 8.2 | |
| 8 days ago | 15 days ago | |
| Python | Python | |
| Apache License 2.0 | 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.
cascade
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modeldb VS cascade - a user suggested alternative
2 projects | 12 Dec 2023
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Sacred VS cascade - a user suggested alternative
2 projects | 5 Dec 2023
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keepsake VS cascade - a user suggested alternative
2 projects | 5 Dec 2023
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aim VS cascade - a user suggested alternative
2 projects | 5 Dec 2023
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guildai VS cascade - a user suggested alternative
2 projects | 5 Dec 2023
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metaflow VS cascade - a user suggested alternative
2 projects | 5 Dec 2023
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clearml VS cascade - a user suggested alternative
2 projects | 5 Dec 2023
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cascade alternatives - clearml and MLflow
3 projects | 1 Nov 2023
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Announcing Cascade
This is Cascade - very lightweight MLE solution for individuals and small teams
deepchecks
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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
-
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
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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?
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
great_expectations - Always know what to expect from your data.
NVTabular - NVTabular is a feature engineering and preprocessing library for tabular data designed to quickly and easily manipulate terabyte scale datasets used to train deep learning based recommender systems.
evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
powershap - A power-full Shapley feature selection method.
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.
zenml - ZenML 🙏: Build portable, production-ready MLOps pipelines. https://zenml.io.
feast - The Open Source Feature Store for Machine Learning
ds2 - Easiest way to use AI models without coding (Web UI & API support)
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.
FeatureHub - The most comprehensive library of AI/ML features across multiple domains. Our goal is to create a dataset that serves as a valuable resource for researchers and data scientists worldwide
giskard - 🐢 Open-Source Evaluation & Testing for LLMs and ML models