deepchecks
great_expectations
Our great sponsors
deepchecks | great_expectations | |
---|---|---|
15 | 15 | |
3,295 | 9,361 | |
2.7% | 1.9% | |
8.6 | 9.9 | |
6 days ago | 7 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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.
deepchecks
-
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?
-
Data Validation tools
I use DeepChecks for my continuous training pipelines. You can check out the Data Integrity Checks.
-
How to trust your machine learning model with Deepchecks
Deepchecks (https://github.com/deepchecks/deepchecks) is an open-source Python package for comprehensively validating your machine learning models and data with minimal effort. This includes checks related to various types of issues, such as model performance, data integrity, distribution mismatches, and more.
Explore the docs https://docs.deepchecks.com
- [P] Deepchecks: an open-source tool for high standards validations for ML models and data.
great_expectations
-
Data Quality at Scale with Great Expectations, Spark, and Airflow on EMR
Great Expectations (GE) is an open-source data validation tool that helps ensure data quality.
- Looking for Unit Testing framework in Database Migration Process
-
Soda Core (OSS) is now GA! So, why should you add checks to your data pipelines?
GE is arguably the most well known OSS alternative to Soda Core. The third option is deequ, originally developed and released in OSS by AWS. Our community has told us that Soda Core is different because it’s easy to get going and embed into data pipelines. And it also allows some of the check authoring work to be moved to other members of the data team. I'm sure there are also scenarios where Soda Core is not the best option. For example, when you only use Pandas dataframes or develop in Scala.
-
Package for drift detection
great_expectations: https://github.com/great-expectations/great_expectations
-
Data pipeline suggestions
Testing: GreatExpectations
-
Where can I find free data engineering ( big data) projects online?
Ingestion / ETL: Airbyte, Singer, Jitsu Transformation: dbt Orchestration: Airflow, Dagster Testing: GreatExpectations Observability: Monosi Reverse ETL: Grouparoo, Castled Visualization: Lightdash, Superset
- [P] Deepchecks: an open-source tool for high standards validations for ML models and data.
-
great_expectations VS redata - a user suggested alternative
2 projects | 24 Sep 2021
-
Looking for open-source model serving framework with dashboard for test data quality
it should have a dashboard for test data quality monitoring - ideally with alarms from the great_expectations framework https://github.com/great-expectations/great_expectations
-
[D] What’s the simplest, most lightweight but complete and 100% open source MLOps toolkit? -> MY OWN CONCLUSIONS
I expected Great Expectations library to be recommended, but nobody told anything. Instead, unit testing and/or smoke tests using pytest. And checking them with Jenkins. Anyway, if Kedro ends up being our project template, I'll keep an eye on the plugin with Great Expectations.
What are some alternatives?
evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
kedro-great - The easiest way to integrate Kedro and Great Expectations
re_data - re_data - fix data issues before your users & CEO would discover them 😊
streamlit - Streamlit — A faster way to build and share data apps.
seldon-core - An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
soda-core - :zap: Data quality testing for the modern data stack (SQL, Spark, and Pandas) https://www.soda.io
feast - Feature Store for Machine Learning
dbt-core - dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.
Poetry - Python packaging and dependency management made easy
dataprep - Open-source low code data preparation library in python. Collect, clean and visualization your data in python with a few lines of code.