evidently
great_expectations
Our great sponsors
evidently | great_expectations | |
---|---|---|
10 | 15 | |
4,539 | 9,361 | |
4.2% | 1.9% | |
9.5 | 9.9 | |
7 days ago | 5 days ago | |
Jupyter Notebook | Python | |
Apache License 2.0 | 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.
evidently
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Batch ML deployment and monitoring blueprint using open-source
Repo:https://github.com/evidentlyai/evidently/tree/main/examples/integrations/postgres_grafana_batch_monitoring
- Looking for recommendations to monitor / detect data drifts over time
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State of the Art data drift libraries on Python?
Thank you for your answer. I'm trying it today and the the other libraries mentioned + https://github.com/evidentlyai/evidently
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Package for drift detection
evidently: https://github.com/evidentlyai/evidently
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The hand-picked selection of the best Python libraries released in 2021
Evidently.
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[D] 5 considerations for Deploying Machine Learning Models in Production β what did I miss?
Consideration Number #5: For model observability look to Evidently.ai, Arize.ai, Arthur.ai, Fiddler.ai, Valohai.com, or whylabs.ai.
great_expectations
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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
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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 itβs 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.
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Package for drift detection
great_expectations: https://github.com/great-expectations/great_expectations
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Data pipeline suggestions
Testing: GreatExpectations
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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.
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great_expectations VS redata - a user suggested alternative
2 projects | 24 Sep 2021
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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
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[D] Whatβs 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?
kedro-great - The easiest way to integrate Kedro and Great Expectations
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.
re_data - re_data - fix data issues before your users & CEO would discover them π
streamlit - Streamlit β 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
MLflow - Open source platform for the machine learning lifecycle
whylogs - An open-source data logging library for machine learning models and data pipelines. π Provides visibility into data quality & model performance over time. π‘οΈ Supports privacy-preserving data collection, ensuring safety & robustness. π
ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
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