Make Better Decisions, Faster
Unique Data Structure, Optimized for ML Operations
DataHeroes uses a unique Coreset Tree data structure, optimized for ML training and tuning, that allows you to train and tune your model hundreds of times faster and cheaper without reducing model quality. Use the DataHeroes Python library to easily convert your training dataset to a Coreset Tree structure and perform all model training and tuning with significantly less compute.
service_obj = CoresetTreeServiceDTC(
data_params=data_params,
optimized_for=”training”,
n_instances=XXX,
model_cls=XGBClassifier,
)
# Build the Coreset Tree.
service_obj.build_from_df(datasets=df)
Easily Add New Data to your Model, without Creating Bias
New data can easily be added to an existing Coreset Tree structure, and the model can be updated in minutes, without creating bias. Similarly, data can be removed or changed, with the model updating in seconds, without retraining your full dataset.
CEO at AI Fund
Improve Model Quality
Use DataHeroes to retrain your model more frequently and test more hyperparameter combinations, to ensure you have the most accurate and up-to-date model in production.
Reduce Compute Costs and Emissions
Training and tuning your model with DataHeroes consumes orders of magnitude less compute resources, allowing you to retrain and tune more frequently while lowering costs and emissions.
Real Time Model Training
Retrain and tune your model monthly, weekly, daily or multiple times per day in just a few minutes, whether your dataset is small or massive.
Blazing Fast Hyperparameter Tuning
Evaluate hundreds of hyperparameter combinations in minutes, either on-prem, on your virtual private cloud, or on your own machine.
Ready to Get Started?
Our Blog
Stay updated with our latest blog posts, news, and announcements by reading here.
Hyperparameter Tuning Methods Every Data Scientist Should Know
Unleashing the Power of ML: The Art of Training Models and Its Vital Significance
