Problem

Our team was tasked with running various open source AI models for generative image, text, and audio locally. We needed to train these models on custom data using LORAs.

The problem we faced was that the process of training these models was time-consuming and required a lot of computational resources. Additionally, we needed to ensure that the models were accurate and producing high-quality results.

Solution

To solve this problem, we decided to use cloud computing services to run the AI models. This allowed us to access more computational resources and speed up the training process.

We also implemented a quality control system that checked the accuracy and quality of the model's output. This helped us ensure that our models were producing high-quality results.

Learn more about our solution here.

Impact

Reduced training time by X%
Improved accuracy of model output by X%

"Quote of how incredible of an impact this had."

Team

Team Member #1 - Machine Learning Engineer
- Built and trained custom AI models using LORAs
- Implemented quality control system for model output
Team Member #2 - Cloud Computing Specialist
- Set up cloud computing services for running AI models
- Optimized computational resources for training models

Cloud Computing Boosts AI Model Training & Quality