Automated machine learning allows businesses to efficiently train machine learning models without requiring extensive technical knowledge. By defining your target variables, the AI can focus its learning processes towards understanding these specific tasks.

With a variety of industry-specific suggestions, you can quickly determine which variables are most important for your business.

Our pre-suggested training regimens offer estimated processing requirements and rules for re-training after new data is imported. The suggested frequency for retraining is determined by the volume of new data being imported and the processing requirements of the models, ensuring optimal performance.

The Training Hub automatically trains models for regression, classification, and clustering problems, while also encoding, scaling, imputing, and selecting features to optimize performance.

You can easily keep track of processing timelines and training progress to ensure that your models are always up-to-date and performing at their best. Easily save and store trained models for later use.

By labelling models according to their version and lineage, you can quickly categorize and group them by their relevance to a particular family of data or project line. This makes it easy to deploy models on out-of-sample data sets in the Testing Studio, and to quickly gain insights that can help drive business decisions.