Labelbox•August 21, 2023
It’s easy to imagine how LLMs can accelerate the development of common enterprise AI applications like product categorization, content recommendation, or sentiment analysis. But for many businesses, the AI solutions that will create the most value involve more specialized and domain-specific use cases — and even though foundation models are trained on more data than most organizations can reasonably collect, they may not achieve the results required for these specific projects.
Even so, these powerful off-the-shelf models can help enterprise teams move faster along the path to powering applications with AI. Read on to discover the benefits of incorporating foundation models into your specialized AI application development workflow.
One of the primary stumbling blocks of building specialized AI applications is that typically, the amount of data available for these use cases isn’t nearly enough to train a custom model to the required performance level. The lack of sufficient training data can significantly delay an AI project. By leveraging the right foundation model, however, teams can circumvent this issue.
Foundation models are powerful because they are trained on internet-level data, making them ideal for transfer and few-shot learning. By giving the right model just a few labeled examples for your use case, you can create a baseline ML model. For instance, a team working on a plant pathology use case could fine-tune an image classification model such as YOLOv8 on just a few hundred labeled images of healthy and diseased crop samples. The foundation model provides general visual understanding, which is then specialized for your specific task.
Foundation models can dramatically reduce the engineering effort required to build AI systems, enabling teams to skip many time-consuming tasks and focus their energy on the end application. Here are a few key ways that these models streamline the development process:
Foundation models encapsulate a tremendous amount of solved tasks in a reusable package. AI teams spend less time rebuilding common capabilities from scratch. With proven architectures, features, and performance, they can focus on rapidly building and delivering end applications. The productivity boost enables faster iteration and innovation.
Labeling even a relatively small amount of data for AI development can be slow and expensive if done by humans, especially when domain experts are involved for specialized use cases. Leading AI teams have proven that pre-labeling your data — that is, using a model or program to take a first pass at a labeling task — can significantly cut labeling time and expenses. Teams that use a powerful and accurate model for pre-labeling can realize 50% or more in labeling costs. With the advent of foundation models, such high-level pre-labeling workflow is accessible to any AI team, not just those with large budgets and advanced architectures.
Even if your specialized use case cannot be entirely served by a foundation model, the right model can help your team automate your labeling process. When your labelers need not start from scratch with your data, and reviewers and domain experts only need to review a few samples to maintain labeling quality, the result is not only labeling time and cost savings, but a faster path to production AI.
Learn more about how you can leverage foundation models for AI development quickly and easily with Model Foundry — and be sure to sign up and get access via the waitlist while you’re there.