Design, train, and evaluate ML models hassle-free, thanks to the Striveworks MLOps platform. Our no-code interface lets you load datasets, annotate data, and configure training hyperparameters—all with the click of a button.
Automated dataset versioning lets you audit and reproduce your training runs effortlessly, freeing your team to focus on creating, optimizing, and launching models into production.
Our no-code interface makes it fast and easy to create, fine-tune, and share new AI models.
Out-of-the-box versioning lets you experiment quickly while keeping a clear audit trail for compliance. Our data lineage process automatically tracks all manipulations of datasets: additions, splits, annotations, and more.
Easily find and reuse datasets by searching and filtering the metadata.
Get high-code training options with no-code effort. The Striveworks training wizard applies best practices for model training by default. Simply load a dataset, set your hyperparameters, and go.
Load, sort, and search your data catalog easily in our no-code interface.
The Striveworks annotation studio makes data labeling a breeze. Model-in-the-loop support brings AI-powered hinting into the training process, making your analysts up to 40 times faster.
Get suggested bounding boxes to confirm object detections in seconds.
Highlight text to apply categories for NLP.
Integrate your preferred annotation tool to create a custom workflow.
Neural networks—and, more broadly, most predictive models—perform classification, regression, or some combination of the two. Read about the concepts involved in training, without diving deeply into the details of the mathematics.
Neural network models are often over-parameterized and frequently contain millions or even billions of learned parameters. However, in most cases, neural network pruning is one method for simplifying a neural network model.
Now that you have trained great AI models, the fun can begin. Compare, select, and deploy the best model for your production data with the push of a button.