Model Retraining

Retrain and tailor AI models on your own data to achieve peak performance

Models need tailoring. That's why the Modzy platform enables AI model retraining for peak performance.

AI model retraining is crucial to keeping models up-to-date and performant on new 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 retraining machine learning models 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 an AI model, that was already trained, on your own dataset, which makes the model tailored to, and more performant, on your dataset.

AI Model Retraining at Modzy

machine learning model retraining

At Modzy, we’ve pioneered a novel approach for AI model retraining based on transfer learning. This approach truly enables automated model retraining, allowing data scientists to quickly and easily retrain machine learning models on their own data for improved performance, enabling major time and cost savings.

  • Our AI model 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. 

During training, we use large datasets from a wide range of distributions to ensure that our models can make correct predictions across diverse scenarios. Our machine learning model retraining solution improves performance and minimizes the requirement for a large labeled dataset, which reduces the amount of computational resources and time required for training.

Modzy Retraining Highlights

Today, our solution allows for retraining a model on a dataset of 1600 images on one GPU in 16 minutes

Retrained models detect people at a 45% mean average precision (as compared to a 5% benchmark), and vehicles at a 76% mean average precision (as compared to a 20% benchmark)

Problems with Existing Approaches to Retraining

There are several challenges with existing approaches to retrain machine learning 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 AI model retraining offers a solution to many of the challenges with existing workaround solutions to retraining, enabling resource efficiencies and better performant solutions.