Prevision

Prevision

Prevision.io
TabFM

TabFM

Google
+
+

Related Products

  • Fraud.net
    56 Ratings
    Visit Website
  • Google AI Studio
    26 Ratings
    Visit Website
  • Gemini Enterprise Agent Platform
    967 Ratings
    Visit Website
  • Google Cloud BigQuery
    2,016 Ratings
    Visit Website
  • Teradata VantageCloud
    1,120 Ratings
    Visit Website
  • RunPod
    211 Ratings
    Visit Website
  • dbt
    259 Ratings
    Visit Website
  • MuukTest
    34 Ratings
    Visit Website
  • Epsilon3
    265 Ratings
    Visit Website
  • Google Cloud Speech-to-Text
    365 Ratings
    Visit Website

About

Building a model is an iterative process that can take weeks, months, or even years, and reproducing model results, maintaining version control, and auditing past work are complex. Model building is an iterative process. Ideally, you record not only each step but also how you arrived there. A model shouldn’t be a file hidden away somewhere, but instead a tangible object that all parties can track and analyze consistently. Prevision.io allows you to record each experiment as you train it along with its characteristics, automated analyses, and versions as your project progress, whether you created it using our AutoML or your own tools. Automatically experiment with dozens of feature engineering strategies and algorithm types to build highly performant models. In a single command, the engine automatically tries out different feature engineering strategies for every type of data (e.g. tabular, text, images) to maximize the information in your datasets.

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.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Developer teams and companies seeking a solution to track, record, and compare the iterations of ML experiments

Audience

Data scientists and analytics teams that need accurate tabular classification and regression without repetitive training, tuning, and feature-engineering work

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

No information available.
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Prevision.io
France
prevision.io

Company Information

Google
Founded: 1998
United States
research.google/blog/introducing-tabfm-a-zero-shot-foundation-model-for-tabular-data/

Alternatives

Alternatives

MLBox

MLBox

Axel ARONIO DE ROMBLAY
T5

T5

Google

Categories

Categories

Machine Learning Features

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

Integrations

SkyStem ART

Integrations

SkyStem ART
Claim Prevision and update features and information
Claim Prevision and update features and information
Claim TabFM and update features and information
Claim TabFM and update features and information