Models need tailoring. That's why Modzy platform gives you tools to retrain models on your custom data.
While humans have a natural ability to transfer knowledge and experience across different tasks and domains, machine learning models still struggle with generalizability. This means that countless hours and resources are spent training models from scratch to perform against slightly different datasets, rather than leveraging existing models and retraining them to be more generalizable. Transfer learning refers to the concept of improving a machine learning model’s performance by transferring the knowledge acquired during training to a different, but related, dataset. In simpler terms, transfer learning allows you to quickly retrain a model, that was already trained, on your own dataset, which makes the model more performant on your dataset.
Retraining at Modzy
- Our retraining solution, which is different than simply training a model from scratch, is engineered and designed to consume as little computational power in as short a time as possible given the size of the target dataset and task.
- Our solution allows for retraining of object classification models, a solution to a much trickier problem. Retraining image classification models is easy, and solutions have existed for this problem for years.
Problems with Existing Approaches to Retraining
There are several challenges with existing approaches to retrain models :
- Models needs to be trained on large dataset of labeled data points to be generalizable
- Collecting a sufficient sample of labeled training data is expensive, time-consuming, or may be impossible for some scenarios
- Current approach primarily involves training new models from scratch to do slightly different tasks, with high infrastructure and data curation costs
Modzy’s approach to retraining offers a solution to many of the challenges with existing workaround solutions to retraining, enabling resource efficiencies and better performant solutions.