TabFMGoogle
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About
Discover & trust data for your analysis and models. Be more productive by breaking silos. Get immediate context into the data and see how others are using it. Search for data within your organization by a simple text search. A PageRank-inspired search algorithm recommends results based on names, descriptions, tags, and querying/viewing activity on the table/dashboard. Build trust in data using automated and curated metadata, descriptions of tables and columns, other frequent users, when the table was last updated, statistics, a preview of the data if permitted, etc. Easy triage by linking the ETL job and code that generated the data. Update tables and columns with descriptions, reduce unnecessary back and forth about which table to use and what a column contains. See what data fellow co-workers frequently use, own or have bookmarked. Learn what most common queries for a table look like by seeing dashboards built on a given table.
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About
TabFM is a zero-shot foundation model for tabular data, designed to simplify classification and regression workflows that traditionally require manual model training, hyperparameter tuning, and domain-specific feature engineering. Built specifically for tables, TabFM reframes tabular prediction as an in-context learning problem: instead of fitting a new supervised model to each dataset, it takes historical training examples and target testing rows together as one unified prompt, then interprets relationships between columns and rows at inference time. Because tables are two-dimensional and orderless, TabFM uses a hybrid architecture that combines alternating row and column attention, row compression, and a dedicated Transformer for in-context learning over compressed row embeddings. This design lets the model capture complex feature interactions and dependencies while keeping prediction computationally efficient for larger datasets.
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Audience
Enterprises wanting to manage their data with an open source data discovery and metadata engine
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Audience
Data scientists and analytics teams that need accurate tabular classification and regression without repetitive training, tuning, and feature-engineering work
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Support
Phone Support
24/7 Live Support
Online
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Support
Phone Support
24/7 Live Support
Online
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API
Offers API
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API
Offers API
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Screenshots and Videos |
Screenshots and Videos |
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Pricing
No information available.
Free Version
Free Trial
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Pricing
Free
Free Version
Free Trial
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Reviews/
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Reviews/
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Training
Documentation
Webinars
Live Online
In Person
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Training
Documentation
Webinars
Live Online
In Person
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Company InformationAmundsen
United States
www.amundsen.io
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Company InformationGoogle
Founded: 1998
United States
research.google/blog/introducing-tabfm-a-zero-shot-foundation-model-for-tabular-data/
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Categories |
Categories |
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Integrations
AWS Glue
Amazon Redshift
Amazon Web Services (AWS)
Apache Cassandra
Apache Druid
Apache Hive
Apache Spark
Datafold
Delta Lake
Elasticsearch
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Integrations
AWS Glue
Amazon Redshift
Amazon Web Services (AWS)
Apache Cassandra
Apache Druid
Apache Hive
Apache Spark
Datafold
Delta Lake
Elasticsearch
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