Insufficient data causing
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
Model bias due to lack of a wider data from human bias or data collection
ML models learning from noise and inaccuracies as the data set is too large
Making wrong predictions due to high bias and low variance with small dataset
Data collection issues from all sources due to privacy and other restrictions
Training on irrelevant low quality data leading to model issues
Insufficient values in a data set impacting ML model performance
Unable to access
crucial data due to the risk in data security
on data transfer and
storage for ML
How we help?
We assess your AI models for underfitting and overfitting to identify training data gaps
We build federated training models and use STADLE to collaboratively train your AI models for better performance
We teach you on how you can use STADLE to address your training data gaps
How STADLE Helps
STADLE helps you to assess models for performance and identify training data gaps and conditions
Use STADLE APIs to easily build federated learning models that can train your AI models for further performance improvement
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.
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.
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.