Summary
Companies are navigating the ever-changing landscape of AI with a growing need for efficient and cost-effective solutions. As a company that is adapting AI and is under time pressure to decide what approach to take, it's easy to quickly get lost between Large Language Models (LLMs), traditional machine learning models, Small Language Models (SLMs), agentic AI, etc.
In this blog, we attempt to help you make a more informed decision. We focus on LLMs and explore their unique advantages compared to traditional machine learning methods. We argue that they are a superior choice when it comes to investing in AI.
Introduction
We discuss how LLMs not only address the shortcomings of conventional machine learning but also offer game-changing advantages by minimizing the number of models needed for various tasks, cutting experimentation and long-term maintenance costs, reducing development time, and offering an unprecedented generalization.
LLMs vs. Traditional ML
Traditional machine learning is known for expensive and time-consuming experimentation cycles, costly maintenance, and specialized models with limited generalization outside their predefined tasks.
Companies that have tried adopting AI in previous years are well familiar with the pain and costs of traditional machine learning and its inherent challenges. Pains and expenses that, thanks to LLMs, could, to a large extent, be a thing of the past.
LLMs represent a paradigm shift away from traditional machine learning and the challenges inherent in traditional machine learning. As a company that is adapting AI, you don't have to worry about long-running, costly experimentation cycles and the maintenance of tens or even hundreds of models anymore.
One of the great innovations that have led LLMs to be able to generalize so well and allow one LLM to replace many models, drastically cutting the costs and efforts needed to maintain and update numerous models, is instruction tuning.
Many models packed in one: Take the phrase Happy cinnamon cat as an example. Traditionally, to classify this sentence into positive/negative sentiments, extract keywords from it, understand what it entails, and translate it to French, you needed four models. Each went through costly training, experimentation, evaluation, and maintenance. With instruction tuning, you just train and maintain one large language model to do all these tasks. You can then instruct it to classify Happy cinnamon cat, translate it into French, decide what it entails, and do many more. It is as if many models are now packed into one. Accordingly, a drastically lower cost of creating, updating, and maintaining can be expected.
So long, feature engineering: Let’s look at feature engineering, another complexity in traditional machine learning. Traditional machine learning often requires meticulous feature engineering, where machine learning experts painstakingly craft features to represent relevant aspects of the data. This process is time-consuming, experimental, domain-specific, not generalizable, and heavily reliant on the engineer's expertise. The challenge lies in extracting meaningful features that best represent the data. Feature engineering started to become less important with the rise of deep and representation learning, but LLMs (built on top of these technologies) have made them almost completely obsolete, practically saving much time and cutting many costs that were associated with this otherwise necessary task.
Significantly less experimentation: Developing any traditional machine learning model involves testing various algorithms, model architectures, training methods, and hyperparameters to find the best model and configuration. A costly iterative process that demands significant time and computational resources. In a standard setup, hundreds of models are often created, and most of them are thrown away. Only a few of them that achieve acceptable performance are kept. Moreover, expansion to new tasks or domains, or even a shift in the data, necessitates restarting the experimentation cycle, posing a bottleneck in adapting to diverse applications. While LLMs have not completely mitigated this issue, they have significantly reduced the experimentation space. There are many fewer combinations to explore, and hence, a much quicker and cheaper experimentation cycle is expected.
Fine-tuning instead of training: Creating a traditional AI model involves a costly, time-consuming, computationally expensive, and experimental training process. Creating a custom LLM, however, only requires fine-tuning. It's a much less time-consuming and less computationally expensive process that adapts a pre-trained model for a specific task or domain. Although pre-training of an LLM is unimaginably expensive, an absolute majority of use cases and businesses do not require pre-training, only fine-tuning.
Thanks to global attention, big tech, and the open-source communities, the past few years have witnessed the release of a significant number of pre-trained LLMs, many of which show remarkable performance. The task is now only to select the right model and fine-tune it, a remarkable simplification from what it was in traditional machine learning.
Conclusion
Large Language Models (LLMs) are changing AI adoption by simplifying workflows and offering unmatched generalization across tasks, which translates to lower costs and higher speeds of adaption. Unlike traditional machine learning, which requires multiple specialized models and costly experimentation cycles, LLMs consolidate tasks into a single model. They also eliminate the need for costly feature engineering and excessive experimentation. With many pre-trained models readily available, businesses can focus on fine-tuning rather than starting from scratch, which makes LLMs a cost-effective, time-efficient, and scalable solution.
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