Best Practices for Deploying LLMs in Production

Large Language Models (LLMs) have taken the world by storm. Thanks to powerful AI models such as OpenAI's GPT-4 and the popularity of products such as ChatGPT, companies are now racing to integrate LLMs into their products.
Guy Eshet
Guy Eshet
Product Manager at Qwak
April 30, 2023
Best Practices for Deploying LLMs in Production


Large Language Models (LLMs) have taken the world by storm. Thanks to powerful AI models such as OpenAI's GPT-4 and the popularity of products such as ChatGPT, companies are now racing to integrate LLMs into their products.

Launching exciting prototypes with generative models may seem easy, however deploying LLMs in production can be a complex process that requires robust infrastructure and proper tools. 

In this article, we will explore the challenges and considerations for deploying LLMs in production, and learn how Qwak is helping customers in various industries to streamline their MLOps workflows and deploy LLMs in production.

What are LLMs?

LLMs are large machine learning models that use deep learning to generate, summarize and understand natural language, text and code. LLMs are trained on vast amounts of data, and demand immense compute power to train and maintain. 

In recent years, there has been a significant advancement in the inference abilities of LLMs. These models are now capable of performing complex tasks such as creative writing, coding, information retrieval, summarization, and many more. LLMs have become valuable tools for a wide range of applications, including chatbots, voice assistants, search and automated content generation, among others.

Closed-source and open-source LLMs

LLMs can be broadly classified into two categories: closed-source and open-source models. 

Closed-source models, such as GPT-4 by OpenAI, LaMDA by Google, and Jurassic by AI21, are proprietary models that are only available through APIs. These models offer limited customization options and control over your data, as the source code and model weights are not available to the public. However, closed-source models are often more powerful than their open-source counterparts, as they are developed by industry leaders with access to vast resources and expertise.

On the other hand, open-source models, such as BLOOM, Alpaca, LLaMA, OpenAssistant, and BERT, are rapidly evolving and catching up. They offer more flexibility and can be deployed on your own infrastructure while being customized to your specific needs. Make sure to check the model licenses before using them. Sme models are available for research purposes only and cannot be used for commercial applications. 

Customizing large language models

Prompt engineering, fine-tuning, and creating chains are three common techniques used to generate output from LLMs.

Prompt Engineering

Prompt engineering involves the art of providing a prompt to the model. This can include a question, sentence, or topic for the model to focus on. The purpose is to guide the model output towards the desired direction. Prompt engineering can help to control the output and ensure that it's relevant and useful.

Model Chains

Creating chains involves using the output of one model as the input for another model, creating a chain of models that work together to generate the final output. This technique is often used to improve the quality and relevance of the output. As an example, LangChain is one of the most common LLM frameworks supporting these use cases. Additionally, AI-Agent frameworks such as Auto-GPT use similar mechanisms to use LLMs to complete complex tasks.

Model Fine Tuning

Fine-tuning involves training an LLM on a specific dataset or task to improve its performance on that particular task. Fine-tuning can help to optimize the model's output for a specific use case or application. For instance, a fine-tuned LLM could be designed to process financial data or expertly handle customer requests.

Fine-tuning models requires significant infrastructure and can be done on managed models like GPT-4, although the inference cost is generally higher.

Fine Tuning on Qwak

To make the process easier, Qwak enables our customers to fine-tune, tag, and track their fine-tuned models more effectively. This helps to ensure better control over the various model versions between different fine-tuning processes.

Additionally, Qwak makes it easier to connect your existing data pipelines with the model fine-tuning process, allowing data science teams to operate faster. 

Integrating LLMs into your products

Incorporating LLMs into your products can be challenging, as it involves considerations such as privacy, security, and legal issues. These concerns vary widely when choosing to deploy in-house LLMs or when using external models.

In the near future, it's possible that companies will use external GPT-4-like models for early prototyping and high-stakes applications, while opting for smaller, more fine-tuned open-source, self hosted LLMs for other use cases. This approach will likely lead to a mix of both large and small models in the production pipeline.

About Qwak

Qwak is a fully managed, accessible, and reliable ML Platform. It allows builders to transform and store data, build, train, and deploy models, and monitor the entire Machine Learning pipeline. Pay-as-you-go pricing makes it easy to scale when needed.

Chat with us to see the platform live and discover how we can help simplify your journey deploying AI in production.

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