The Challenges of Fine-Tuning Large Language Models and Deploying to Production

Imagine you're part of a team tasked with building a cutting-edge AI application for your company. You've experienced the incredible potential of large language models (LLMs) like those from OpenAI and Google, but you're hesitant to rely on third-party tools for such a sensitive project. You decide to take matters into your own hands and fine-tune an LLM in-house.

At first, the possibilities seem endless. But as you dive in, you quickly realize that fine-tuning an LLM is no simple feat. Gathering high-quality training data, wrangling with computational constraints, and figuring out how to actually deploy the model prove to be significant hurdles. And even once you have a working prototype, you encounter unexpected issues like biased outputs and strange errors.

Welcome to the wild world of fine-tuning LLMs! It's a journey full of challenges, but also incredible opportunities for those willing to put in the work.

The Downsides of Fine-Tuning

Fine-tuning an LLM to handle a specific task or dataset is a powerful technique, but it comes with some notable drawbacks:

• Cost and time: Training these massive models requires serious computational horsepower . For smaller teams or those on a budget, the costs can quickly become prohibitive.
• Brittleness: Fine-tuned models can struggle to adapt to new information without expensive retraining. They're locked into a "fixed snapshot" of the training data.
• Expertise needed: Developing and maintaining cutting-edge AI isn't for the faint of heart. You need specialised skills and knowledge that can be hard to come by.
• Quirky outputs: Models can sometimes "hallucinate" strange or biased results, or completely forget their previous training. Keeping them in line is an ongoing challenge.

In short, fine-tuning is a powerful but demanding technique. But for many, the potential benefits outweigh the costs.

The Challenges of MLOps / LLMOps

Deploying a fine-tuned model is just the beginning. To keep it running smoothly in the real world, you need to wrangle with a host of operational challenges that come with machine learning in production:

• Orchestration and automation: Streamlining the deployment process and building robust CI/CD pipelines for your models can be a major hurdle. You need to efficiently handle the entire lifecycle from training to deployment to monitoring and back again.
• Infrastructure complexity: Managing the infrastructure for deploying models is no simple task. You have to deal with issues like secret management, caching model checkpoints, and finding the right hardware and software setup for inference. It's a complex landscape to navigate.
• Performance and reliability: Once your model is out in the wild, you need to make sure it's performing well and staying reliable. That means keeping a close eye on things like throughput, latency, and error rates. You also need strong versioning practices to manage model updates over time.
• Monitoring and debugging: When something goes wrong with a deployed model, tracking down the issue can be tricky. You need powerful tools for monitoring model performance, analysing errors, and handling unexpected failures gracefully.
• Continuous improvement: The most impactful models are never "done" - they keep getting better over time with fresh data and feedback. But building this kind of continuous improvement loop is easier said than done, especially with the tools available today.

The Unique Challenges of Fine-Tuning at Scale

On top of the general challenges of MLOps, fine-tuning LLMs at scale comes with its own unique set of hurdles:

• Integration headaches: Fitting a powerful but quirky AI model into your existing systems is rarely a smooth process. Model registries and deployment platforms often lack the flexibility to handle the unique needs of fine-tuned LLMs and your pre-existing services might not be optimised for the requirements of LLMs.
• Data management: Fine-tuning models relies heavily on high-quality, domain-specific data. But managing this data pipeline and ensuring the training data is always fresh and relevant is a major challenge, especially as the model scales up. These data challenges are significant, and practicioners have testified that as much as 90% of the effort involved in finetuning can be tied up in this data quality improvement work.
• Experimentation bottlenecks: Efficiently testing different models and configurations is key to finding the right setup for your use case. But wrangling with massive datasets and limited compute resources can really slow down the experimentation process. Quickly switching between local and cloud runs is non-trivial when large datasets are involved and the tooling doesn’t always close the gap between to make it easy to see how to apply insights from local tests into the production end-product or candidate model.
• Feedback loops: To really get the most out of fine-tuning, you might need tight feedback loops between the model and a human in the loop process. But building these human in the loop pipelines is tricky, and the tools to make it seamless are still maturing.

The MLOps ecosystem is rapidly evolving to meet these challenges, but there's still a lot of work to be done to make fine-tuning LLMs at scale a smooth and seamless process.

The Power and Potential

Despite the challenges, fine-tuning LLMs offers immense potential for businesses willing to invest the time and resources:

• Tailor-made performance: A fine-tuned model can be exquisitely adapted to your specific use case, unlocking major gains in accuracy and efficiency compared to one-size-fits-all models.
• Harnessing domain expertise: Fine-tuning lets you leverage your proprietary data and hard-won domain knowledge to build AI assistants uniquely suited to your needs.
• Flexibility and control: With an in-house model, you're in the driver's seat when it comes to model performance, cost management, and alignment with business goals.

In the end, LLMs are incredibly powerful tools, but they're not a magic wand. To wield them effectively, you need to be prepared to roll up your sleeves and embrace the challenges of fine-tuning and MLOps.

But for teams with the right skills and mindset, the payoff can be transformative. Fine-tuned LLMs may require some extra sweat, but they offer a clear path to pushing the boundaries of what's possible with AI.

At ZenML , we've been working internally to build features that enable data teams to tackle the challenges of finetuning LLMs (and other GenAI-type models). If you'd like to keep posted, please subscribe to the ZenML newsletter : https://www.zenml.io/newsletter-signup

Thank you Alex S. and Zuri Negrín for your help in this article!