Fine-tuning Foundational AI Models is Too Costly and Complex for Most Enterprises

We're well into the AI hype cycle. Enterprises across industries are scrambling to understand how they can leverage artificial intelligence to augment their businesses.

Unfortunately, enterprises are failing 80-90% of the time as they try to implement AI into their business. Oftentimes this means they don’t reach ROI or their AI projects simply can’t get to launch due to internal friction.

In general, innovative enterprises will take one of two approaches to AI deployment within their organizations. They either A) train an AI model from scratch or fine-tune an open-source model in-house or B) deploy third-party tools, often point solutions, within existing business processes.

Too often these approaches begin from a mindset that "AI" is the panacea for complex operations problems. In this blog post, we will focus on the challenges enterprises face when taking these approaches to leverage AI.

Let’s start with Challenge #1.

Challenge #1: In-House AI Training and Fine-Tuning Is Too Costly and Complex

The organizational requirements for training an enterprise-scale AI model from scratch or fine-tuning an existing model for a specific use case are extremely high. The main barriers to building a successful model or effectively fine-tuning one are cost and complexity.

Costs of Training An In-House Model

Costs for building a large-scale model like a large language model (LLM) reportedly start around $2-4 million, with millions in recurring costs to run them. These costs include expert AI talent, human data trainers, infrastructure, and compute.

Enterprise leaders can be lured into spearheading in-house AI model training projects that either don’t yield an effective model or creep far past their forecasted costs. That’s because of an outdated approach to model development.

In the beginning, AI researchers trained large language models (LLMs) using data easily collected on the internet. However, just because data is easily available does not mean it is honest, helpful, or harmless to the user.

On the contrary, data easily collected from the internet can be harmful, biased, and toxic, the consequences of which can be worse for the enterprise than an ineffective model. Like a child, these nascent models require supervision and reinforcement of good behavior in order to align with user preferences.

Supervised fine tuning (SFT) and reinforcement learning (RL) are the methods used by researchers to remove bias and harmful answers from responses generated by LLMs. But performing these techniques to align an AI model is too costly for enterprises to perform in-house.

Thus, enterprise leaders can face project failure or perpetually chase ROI by exclusively managing AI model training internally.

Costs for Fine-Tuning an Existing Model

Fine-tuning an existing model is much cheaper. But the investment is still larger than enterprises might anticipate.

One of the most burdensome costs enterprises will incur in fine-tuning is data preparation. That’s because out-of-the-box AI tools don’t fully support complex business processes.

To effectively support a specific use case, open-source models will need to be fine-tuned on cleanly structured human-in-the-loop data. Enterprises will often chase fast AI solutions by feeding AI models with poorly structured, incomplete datasets that produce unuseful outputs.

A more effective data preparation approach in the fine-tuning lifecycle first involves a strong data preprocessing phase. Data preprocessing is a largely manual process that turns unstructured data into a dataset ready for an AI model to be trained on.

Then, the model will need to be fine-tuned on specifically created datasets, like demonstration data, to align the model with the needs of the specific use case. An example of demonstration data is a dataset of thousands of meticulously crafted conversation examples between a person and a chatbot for a customer support use case.

These steps become too costly for internal teams because they can’t be automated. People-driven tasks don’t scale well.

How Invisible Solves the Cost And Complexity Problem

Scaling AI training and fine-tuning is complex and labor-intensive. Most enterprises aren’t equipped to do it without outside help.

Many enterprise leaders will be familiar with and turn to BPOs. However, traditional BPOs can’t provide the quality of human-in-the-loop data required to produce or fine-tune an effective model.

Invisible provides a complete AI training solution for enterprises that are building foundational models or fine-tuning open-source models for specific use cases. Our platform is 100% configurable to enable cost-effective, human-in-the-loop AI training and fine-tuning at scale.

Our global team of AI trainers receives steps unique to their expertise through our platform. Each step is performed via a simple user interface specifically designed for that step’s goal.

See that capability in action with an example of a Reinforcement Learning from Human Feedback (RLHF) step on our platform:

In this example, an AI trainer provides a prompt and then receives a sample of four outputs to rank for accuracy and helpfulness. They can also report if an output is toxic or harmful, providing metadata for why that’s the case.

Without a solution like Invisible, enterprises are forced to build this infrastructure themselves and hire expensive talent, or turn to a BPO that provides poor-quality data.

Ready to harness AI’s potential? Speak to the team.