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Showing 21 open source projects for "you-get"
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Atera all-in-one platform IT management software with AI agentsAtera’s AI agents don’t just assist, they act. From detection to resolution, they handle incidents and requests instantly, taking your IT management from automated to autonomous.
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Desktop and Mobile Device Management SoftwareDesktop Central is a unified endpoint management (UEM) solution that helps in managing servers, laptops, desktops, smartphones, and tablets from a central location.
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Dagster
An orchestration platform for the development, production
Dagster is an orchestration platform for the development, production, and observation of data assets. Dagster as a productivity platform: With Dagster, you can focus on running tasks, or you can identify the key assets you need to create using a declarative approach. Embrace CI/CD best practices from the get-go: build reusable components, spot data quality issues, and flag bugs early. Dagster as a robust orchestration engine: Put your pipelines into production with a robust multi-tenant, multi-tool engine that scales technically and organizationally. ... -
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FiftyOne
The open-source tool for building high-quality datasets
...Nothing hinders the success of machine learning systems more than poor-quality data. And without the right tools, improving a model can be time-consuming and inefficient. FiftyOne supercharges your machine learning workflows by enabling you to visualize datasets and interpret models faster and more effectively. Improving data quality and understanding your model’s failure modes are the most impactful ways to boost the performance of your model. FiftyOne provides the building blocks for optimizing your dataset analysis pipeline. Use it to get hands-on with your data, including visualizing complex labels, evaluating your models, exploring scenarios of interest, identifying failure modes, finding annotation mistakes, and much more! ... -
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Diffgram
Training data (data labeling, annotation, workflow) for all data types
From ingesting data to exploring it, annotating it, and managing workflows. Diffgram is a single application that will improve your data labeling and bring all aspects of training data under a single roof. Diffgram is world’s first truly open source training data platform that focuses on giving its users an unlimited experience. This is aimed to reduce your data labeling bills and increase your Training Data Quality. Training Data is the art of supervising machines through data. This... -
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Apache Airflow Provider
Great Expectations Airflow operator
Due to apply_default decorator removal, this version of the provider requires Airflow 2.1.0+. If your Airflow version is 2.1.0, and you want to install this provider version, first upgrade Airflow to at least version 2.1.0. Otherwise, your Airflow package version will be upgraded automatically, and you will have to manually run airflow upgrade db to complete the migration. This operator currently works with the Great Expectations V3 Batch Request API only. If you would like to use the operator in conjunction with the V2 Batch Kwargs API, you must use a version below 0.1.0. ... -
Omnilert | Visual Passive Security SystemsTransform your passive security systems into early warning and active prevention systems with Omnilert's visual artificial intelligence. Omnilert Gun Detect not only helps save lives but also reduces the operational, legal, and social impacts of gun violence.
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CleanVision
Automatically find issues in image datasets
CleanVision automatically detects potential issues in image datasets like images that are: blurry, under/over-exposed, (near) duplicates, etc. This data-centric AI package is a quick first step for any computer vision project to find problems in the dataset, which you want to address before applying machine learning. CleanVision is super simple -- run the same couple lines of Python code to audit any image dataset! The quality of machine learning models hinges on the quality of the data used to train them, but it is hard to manually identify all of the low-quality data in a big dataset. CleanVision helps you automatically identify common types of data issues lurking in image datasets. ... -
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SDGym
Benchmarking synthetic data generation methods
...Measure performance and memory usage across different synthetic data modeling techniques – classical statistics, deep learning and more! The SDGym library integrates with the Synthetic Data Vault ecosystem. You can use any of its synthesizers, datasets or metrics for benchmarking. You also customize the process to include your own work. Select any of the publicly available datasets from the SDV project, or input your own data. Choose from any of the SDV synthesizers and baselines. Or write your own custom machine learning model. In addition to performance and memory usage, you can also measure synthetic data quality and privacy through a variety of metrics. ... -
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Encord Active
The toolkit to test, validate, and evaluate your models and surface
...Use the intuitive UI to explore your data or access all the functionalities programmatically. Discover errors, outliers, and edge-cases within your data - all in one open source toolkit. Get a high level overview of your data distribution, explore it by customizable quality metrics, and discover any anomalies. Use powerful similarity search to find more examples of edge-cases or outliers. -
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Cleanlab
The standard data-centric AI package for data quality and ML
...See some of the datasets cleaned with cleanlab at labelerrors.com. This package helps you find label issues and other data issues, so you can train reliable ML models. All features of cleanlab work with any dataset and any model. Yes, any model: PyTorch, Tensorflow, Keras, JAX, HuggingFace, OpenAI, XGBoost, scikit-learn, etc. If you use a sklearn-compatible classifier, all cleanlab methods work out-of-the-box. -
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lakeFS
lakeFS - Git-like capabilities for your object storage
...Data is dynamic, it changes over time. Dealing with that without a data version control system is error-prone and labor-intensive. With lakeFS, your data lake is version controlled and you can easily time-travel between consistent snapshots of the lake. Easier ETL testing - test your ETLs on top of production data, in isolation, without copying anything. Safely experiment and test on full production data. Easily Collaborate on production data with your team. Automate data quality checks within data pipelines. -
All-in-one Home Care Management System designed to improve efficiency, productivity, capacity, and compliance.CareLineLive is an all-in-one cloud-based home care management software founded in 2014 by Josh Hough, who serves as Managing Director. The company is part of MAS Technicae Group and received majority investment from Accel-KKR in June 2025.
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ydata-profiling
Create HTML profiling reports from pandas DataFrame objects
ydata-profiling primary goal is to provide a one-line Exploratory Data Analysis (EDA) experience in a consistent and fast solution. Like pandas df.describe() function, that is so handy, ydata-profiling delivers an extended analysis of a DataFrame while allowing the data analysis to be exported in different formats such as html and json. -
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Gretel Synthetics
Synthetic data generators for structured and unstructured text
...Customize privacy settings so that data is always safe while remaining useful for downstream workflows. Ensure data accuracy and privacy confidently with expert-grade reports. Need to synthesize one or multiple data types? We have you covered. Even take advantage or multimodal data generation. Synthesize and transform multiple tables or entire relational databases. Mitigate GDPR and CCPA risks, and promote safe data access. Accelerate CI/CD workflows, performance testing, and staging. Augment AI training data, including minority classes and unique edge cases. Amaze prospects with personalized product experiences. -
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re_data
re_data - fix data issues before your users & CEO would discover them
re_data is an open-source data reliability framework for the modern data stack. Currently, re_data focuses on observing the dbt project (together with underlying data warehouse - Postgres, BigQuery, Snowflake, Redshift). Gather all relevant outputs about your data in one place using our cloud. Invite your team and debug it easily from there. Go back in time, and see your past metadata. Set up Slack notifications to always know when a new report is produced or an existing one got updated. -
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dbt-re-data
re_data - fix data issues before your users & CEO would discover them
re_data is an open-source data reliability framework for the modern data stack. Currently, re_data focuses on observing the dbt project (together with underlaying data warehouse - Postgres, BigQuery, Snowflake, Redshift). Data transformations in re_data are implemented and exposed as models & macros in this dbt package. Gather all relevant outputs about your data in one place using our cloud. Invite your team and debug it easily from there. Go back in time, and see your past metadata. Set up... -
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Muse: Middleware Universal Scripting idE
Automate: WebSphere; WebLogic; JBoss; Glassfish; Tomcat; Linux, WinRM
Simplify... Aggregate... Automate... Simplify... *** OPEN SOURCE - GPL3/EPL. Use Python / Jython to automate WebSphere, WebLogic, JBoss, Glassfish and Tomcat Middleware Estates over JMX, both SSL and non-SSL + Linux SSH (agent-less) + WinRM Target all 5 servers, Linux and WinRM from the same workspace. Familiar Eclipse based Jython and Python Development IDE, pre-configured and ready to go. 4-Click Installer. Win x64, Linux WINE x64. Built-In JVM. Java 8/9/10, Amazon... -
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NBi
NBi is a testing framework (add-on to NUnit)
...It supports most of the relational databases (SQL server, MySQL, postgreSQL ...) and OLAP platforms (Analysis Services, Mondrian ...) but also ETL and reporting components (Microsoft technologies). The main goal of this framework is to let users create tests with a declarative approach based on an Xml syntax. By the means of NBi, you don't need to develop C# code to specify your tests! Either, you don't need Visual Studio to compile your test suite. Just create an Xml file and let the framework interpret it and play your tests. The framework is designed as an add-on of NUnit but with the possibility to port it easily to other testing frameworks. -
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Feathr
A scalable, unified data and AI engineering platform for enterprise
...It is currently a project under LF AI & Data Foundation. Define data and feature transformations based on raw data sources (batch and streaming) using Pythonic APIs. Register transformations by names and get transformed data(features) for various use cases including AI modeling, compliance, go-to-market and more. Share transformations and data(features) across team and company. Feathr is particularly useful in AI modeling where it automatically computes your feature transformations and joins them to your training data, using point-in-time-correct semantics to avoid data leakage, and supports materializing and deploying your features for use online in production. -
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SQLBucket
Lightweight library to write, orchestrate and test your SQL ETL
...It gives the possibility to set variables and introduces some control flow using the fantastic Jinja2 library. It also implements a very simplistic unit and integration test framework where you can validate the results of your ETL in the form of SQL checks. With SQLBucket, you can apply TDD principles when writing data pipelines. To start working, you need to instantiate your SQLBucket core object with the project_folder parameter. That folder will contain all your SQL ETL. The python file where you create your SQLBucket object is also a good place to instantiate your command line interface. -
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Medrechaincode
Lifetime medical records in decentralized chain code-network
...This platform will also help the healthcare professional to use integrated data quality tool to remove duplicity of patient records , profiling of medical records, metadata discovery , data cleansing , classification of medical records, bucketization, anomaly discovery to find out the anomalous trend of medical records. m This platform will help patient to reduced their consultation time, monetize their medical records, for labs to get the historical records & come up with new medicine and make a clinical trial. -
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apache spark data pipeline osDQ
osDQ dedicated to create apache spark based data pipeline using JSON
This is an offshoot project of open source data quality (osDQ) project https://sourceforge.net/projects/dataquality/ This sub project will create apache spark based data pipeline where JSON based metadata (file) will be used to run data processing , data pipeline , data quality and data preparation and data modeling features for big data. This uses java API of apache spark. It can run in local mode also. Get json example at https://github.com/arrahtech/osdq-spark How to run Unzip the zip file Windows : java -cp .\lib\*;osdq-spark-0.0.1.jar org.arrah.framework.spark.run.TransformRunner -c .\example\samplerun.json Mac UNIX java -cp ./lib/*:./osdq-spark-0.0.1.jar org.arrah.framework.spark.run.TransformRunner -c ./example/samplerun.json For those on windows, you need to have hadoop distribtion unzipped on local drive and HADOOP_HOME set. ... -
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Restful APIs for Data Cleansing
This is sister project for osDQ which provide Restful APIs
(Beta Version) This is sister project for https://sourceforge.net/projects/dataquality/ . It provides Restful APIs for features for data quality and data preparation features. This project will help projects which want embed data quality and data preparation features in their project or UI using restful calls. Data Cleansing APIs Dockerfile: # Pull base image FROM frnde/jetty-9.4.2-jre8-alpine-cet ADD osdq-v0.0.1.war /var/lib/jetty/webapps/osdq.war EXPOSE 8080 Docker... -
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Parse, analyze and -- most importantly -- use COBOL data definitions. This gives you access to COBOL data from Python programs. Write data analyzers, one-time data conversion utilities and Python programs that are part of COBOL systems. Really.
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