The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
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Updated
Jul 5, 2024 - Python
DataOps is an automated, process-oriented methodology, used by analytic and data teams, to improve the quality and reduce the cycle time of data analytics. While DataOps began as a set of best practices, it has now matured to become a new and independent approach to data analytics. DataOps applies to the entire data lifecycle from data preparation to reporting, and recognizes the interconnected nature of the data analytics team and information technology operations.
The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
DataOps for the Modern Data Warehouse on Microsoft Azure. https://aka.ms/mdw-dataops.
Cloud Native DataOps & AIOps Platform | 云原生数智运维平台
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Efficient data transformation and modeling framework that is backwards compatible with dbt.
An open-source data logging library for machine learning models and data pipelines. 📚 Provides visibility into data quality & model performance over time. 🛡️ Supports privacy-preserving data collection, ensuring safety & robustness. 📈
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Open source security data pipelines.
A list of tools for annotating data, managing annotations, etc.
Power BI DevOps & Source Control Tool
One framework to develop, deploy and operate data workflows with Python and SQL.