STADLE 
FEDERATED 
LEARNING

Insufficient data causing

accuracy problems?

Privacy preventing access to key training data?

STADLE increases model performance by solving your training data problems.
 

85% of AI projects will deliver erroneous outcomes due to bias in data - Gartner

Bias

Model bias due to lack of a wider data from human bias or data collection

Overfitting

ML models learning from noise and inaccuracies as the data set is too large

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Underfitting

Making wrong predictions due to high bias and low variance with small dataset

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Data Silos

Data collection issues from all sources due to privacy and other restrictions

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Inconsistency

Training on irrelevant low quality data leading to model issues

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Data Sparsity

Insufficient values in a data set impacting ML model performance

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Data Security

Unable to access

crucial data due to the risk in data security

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Data Storage

Skyrocketing costs

on data transfer and

storage for ML

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How We help

How we help?

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We assess your AI models for underfitting and overfitting to identify training data gaps

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We build federated training models and use STADLE to collaboratively train your AI models for better performance

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We teach you on how you can use STADLE to address your training data gaps