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 

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How STADLE Helps

1

STADLE helps you to assess models for performance and identify training data gaps and conditions

2

Use STADLE APIs to easily build federated learning models that can train your AI models for further performance improvement

3

Manage and orchestrate the federated training process using STADLE intuitive user interface

Privacy by Design

STADLE uses federated collective learning techniques that only gathers intelligence and not the actual personal data.

Personal data remains safe and secure and never will be taken out of the person’s device or to a cloud.

STADLE uses federated collective learning techniques that only gather intelligence and not the actual personal data.

This way the personal data remains safe and secure and stays within person’s devices or local servers.

Train with non-representative data

To create a generalized model with a greater accuracy all types of data that cover different use cases are required to train the model. 

Most times this is very challenging due to the nature of data siloed across systems and across organizations

Unlock the true potential of your machine learning model by increasing access to data that was not otherwise available in your data engineering process. 

Training with no data transfer gives you tremendous opportunity to increase the performance of your AI model by using external data from partners, vendors and customers. 

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Significant reduction in data transfer costs

One of the big bottlenecks for training your AI  is the data transfer costs over the cloud. Data transfer costs consume around about 30% of the entire project. Training your AI model with lesser data might lead to the underfitting of the model.

At the surface level, more data is always a good thing. But your AI model might suffer from overfitting if you feed lot more data that necessary.

STADLE helps you to find the right balance between overfitting and underfitting by segregation of training with data vs training with intelligence.

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Training at the Edge reducing data latency

STADLE accelerates your smart products adoption by training your AI at the edge reducing the data latency.

Your time-sensitive functions in video streaming or autonomous driver systems can respond with a greater precision at a faster pace.