Is insufficient data causing
Are privacy constraints preventing access to key training data?
Transitioning from big data to collective intelligence.
STADLE, the next generation Intelligence-Centric AI platform, increases model performance by solving your training data problems.
The Big Data Problem is Real
Existing Big Data & AI platforms can’t learn simultaneously preserving privacy.
Big Data can’t be uploaded at once, making real-time computing & delivery of intelligence an impossibility & slowness.
Transferring & processing all data in centralized clouds takes time and consumes massive computation power.
Utilizing big data centers and huge computation resources leads costly operation and management.
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
Training on irrelevant low quality data leading to model issues
Data collection issues from all sources due to privacy and other restrictions
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 the limitation of your AI models for model drift and bias issues to identify training data gaps
We help building your federated learning environment and use STADLE for better performance
We teach you on how you can integrate your machine learning process into STADLE platform
How STADLE Helps
Integrate STADLE to break through the limitations of the model quality, maximize 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's 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.
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 may lead to limited performance of the model.
At the surface level, more data is always a good thing. But the training using huge computation resources takes a lot of time and costs.
STADLE orchestrates intelligence only and 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 and increasing training efficiency.
Your time-sensitive functions in video streaming or autonomous driver systems can respond with a greater precision at a faster pace.
Your sensors need not send huge streams of video data to your cloud but just detect anomalies that accelerates the real-time decision making more efficient. Medical imaging devices don't have to transfer sensitive health images rather send only the intelligence required for evaluation.
- Thu, Nov 18OnlineNov 18, 2021, 7:00 PMOnlineWhether you’re familiar with Federated Learning, or are just curious about the many applications of this groundbreaking technology, please join us on Friday, September 17th from 4-5 PM Pacific. Just a few of the topics include: Federated Learning Basics Modeling Approaches Centralized vs Decentral
- Thu, Oct 14OnlineOct 14, 2021, 7:00 PMOnlineWhether you’re familiar with Federated Learning, or are just curious about the many applications of this groundbreaking technology, please join us on Friday, September 17th from 4-5 PM Pacific. Just a few of the topics include: Federated Learning Basics Modeling Approaches Centralized vs Decentral
- Fri, Sep 17OnlineSep 17, 2021, 7:00 PMOnlineWhether you’re familiar with Federated Learning, or are just curious about the many applications of this groundbreaking technology, please join us on Friday, September 17th from 4-5 PM Pacific. Just a few of the topics include: Federated Learning Basics Modeling Approaches Centralized vs Decentral