Creating Custom AI Models
As we discussed in a previous blog post, Custom AI models are useful for automating business activities that are highly repetitive and time-consuming. Some examples of tasks that we deal with frequently are creating chatbots to address customer frequently asked questions, generating email templates, and summarizing long product descriptions. Automating these tasks frees up time that you can be using to grow your business.
However, it's no easy feat to train a large language model (LLM) to do these tasks. Pre-training LLMs from scratch is time and resource intensive: from Ruciński, 2024, it cost $100M for OpenAI to train GPT4, $500K for Mistral AI to train Mistral 7B, and $2M for Meta to train Llama2. Without significant investment or a clear path to how AI will contribute to profitability, is not practical for small business owners and startups to train their own models, especially those that are not selling AI as their core competency. However, we can leverage LLMs for our own businesses in a cost-effective way using two primary methods: transfer learning and fine-tuning.
In this article, we will discuss what transfer learning and fine-tuning are at a high level. We will close out with a comparison of when you should use either method for developing a custom AI model.
Figure 1. Transfer Learning and Fine-Tuning.
Model Pre-training (left). In both scenarios, we are leveraging a model that is pre-trained on a large corpus of data. Transfer learning (middle) involves leveraging the distribution of the original data to solve a problem involving the distribution of a target dataset. With fine-tuning (right), we want to customize the model to be highly performant for a specific task or to be domain specific.
What is Transfer Learning?
Transfer learning is a broad training approach that leverages the knowledge from a pre-trained model for tasks or domains that are very similar to the original pre-trained data distribution.
Say we have a GPT4 model, and we want it perform a sentiment analysis on our e-commerce platform. We can leverage the knowledge from GPT4 to this task, which may or may not involve some additional supervised training or fine-tuning. You can see that transfer learning and fine-tuning are highly related but nuanced concepts.
What is Fine-Tuning?
Fine-tuning is taking transfer learning one step further: we take a pre-trained model, and we give it a smaller dataset for additional training to improve to models' ability with a specific task (Task-Specific Fine Tuning), or to work in a specific domain area (Domain-Specific Fine Tuning).
Let's extend the example above. If we wanted to generate a model that specifically performs sentiment analysis for soap products we sell in our e-commerce platform, we can fine-tune the model to be highly specific for this task and product.
If there's anything you should take away from this, it's that transfer learning is a broader method for extending the knowledge of a pre-trained model, while fine-tuning is a specific activity that is under transfer learning.
6 Considerations For Choosing Between Transfer Learning or Fine-Tuning
While transfer learning and fine-tuning are highly related, they have significant differences in resource and time allocation. Table 1 describes the differences between transfer learning and fine-tuning with respect to specific business considerations.
Table 1. Comparing Transfer Learning and Fine-Tuning for Your Business Needs
| Business consideration | Transfer learning | Fine-tuning |
|---|---|---|
| Objective | Uses the general capabilities of a pre-trained model in a business application. | Performs additional training to tailor a model for specific business needs. |
| Resource allocation | Only paying for the production infrastructure | Need to pay for the development work as well |
| Time-to-market | Can be deployed faster with pre-trained models | It will take evaluation and testing to optimize a fine-tuned model |
| Data availability | No additional data needed | Requires additional data |
| Performance expectations | Performance will vary, but tends to perform worse for specific tasks or domain areas | Generally leads to higher accuracy or more aligned responses for specific tasks or domain areas |
| Scalability and flexibility | What you have is what you get | Can be more adaptable to changing business requirements |
Summary
Transfer learning and fine-tuning are methods that allow you to generate custom AI models for your specific business needs. However, they vary in approach, and there is a tradeoff between resources and performance. Selecting one method versus another depends on your business' unique situation and goals.
Both fine-tuning and transfer learning require experienced data scientists and subject matter experts to successfully develop AI models that are highly performant in production. Our team at Torchstack specializes in developing custom AI models using a unique blend of the latest AI technology and years of experience consulting for tech startups and small businesses.
Please reach out to info@torchstack.ai for a free consultation to discuss your specific needs.
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