Search Results for "teams"

...A modern web UI and search layer make it easy for analysts, engineers, and stewards to find assets, read documentation, and see context such as usage, schema history, and downstream impacts. Governance features such as glossaries, tags, ownership, and policies bring order and accountability without blocking day-to-day work. APIs and SDKs enable teams to integrate DataHub into catalogs, CI pipelines, and automation so metadata becomes part of the development lifecycle rather than an afterthought.

...It uses machine learning to learn how records should be compared, reducing the need for brittle hand-written matching rules. The project is designed for data engineering and analytics teams working on customer 360, supplier 360, deduplication, fuzzy matching, data quality, and golden record workflows. Zingg runs on Apache Spark and can scale to large data lake, warehouse, and cloud platform environments. It supports configuration-driven pipelines where users define input data, match fields, training data, models, and output destinations. ...

Unlock the power of big data with OpenDataDiscovery Platform. Experience seamless end-to-end insights, powered by unprecedented observability and trust - from ingestion to production - while building your ideal tech stack! Democratize data and accelerate insights. Find data that fits your use case and discover hints left by your peers to leverage existing knowledge. Explore tags, ownership details, links to other sources and other information to shorten and simplify data discovery phase....

...DATAGYM is an end-to-end workbench to create, annotate, manage, and export the right training data for your computer vision models. Your image data can be imported into DATAGYM from your local machine, from any public image URL or directly from an AWS cloud S3 bucket. Machine learning teams spend up to 80% of their time on data preparation. DATAGYM provides AI-powered annotation functions to help you accelerate your labeling task. The Pre-Labeling feature enables turbo-labeling – it processes thousands of images in the background within a very short time.

...Understanding the properties of data as it moves through applications is essential to keeping your ML/AI pipeline stable and improving your user experience, whether your pipeline is built for production or experimentation. WhyLogs is an open source statistical logging library that allows data science and ML teams to effortlessly profile ML/AI pipelines and applications, producing log files that can be used for monitoring, alerts, analytics, and error analysis. WhyLogs calculates approximate statistics for datasets of any size up to TB-scale, making it easy for users to identify changes in the statistical properties of a model's inputs or outputs. ...