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MLOps and LLMOps – How do they differ?

By Aleksandra Sidorowicz, Machine Learning Engineer at Future Processing

With the rise in Big Data, followed by the Artificial Intelligence renaissance, many organisations have started considering how to leverage large amounts of data effectively, seamlessly and efficiently. That is how MLOps emerged. However, recently we observed an outbreak of a new technology, called Large Language Models (LLM). In principle LLM are models, so the question is how can we ensure high standards of the LLM solutions using already known methods? 

Introducing MLOps & LLMOps

MLOps is the intersection of processes, people and platforms that enable businesses to gain stable value from machine learning. It streamlines development and deployment via monitoring, validation and governance of machine learning models. By adopting an MLOps approach, data scientists and machine learning engineers can collaborate and increase the speed of model development and production, encompassing experimentation and continuous improvement throughout the machine learning lifecycle.

LLM (Large Language Model) is a type of machine learning model that can perform a variety of tasks, where instructions are given in natural language rather than a code. As they can be used to seamlessly generate images and text, LLMs are an exciting development in the technology sector. 

LLMOps encompasses the practices, techniques and tools used for the operational management of large language models in production environments. Like MLOps, LLMOps requires a collaboration of data scientists, DevOps engineers and IT professionals.

Understanding the Differences

In general, the operational requirements of MLOps can be applied to LLMOps, but there are a variety of differences between the two practices which require a unique approach to training and deployment. Therefore, it’s important for businesses to consider how machine learning workflows change with LLMs.


Whilst most machine learning models are created and trained from scratch, LLM start from a foundation model and are fine-tuned by engineers with new data to improve performance. The reason behind this is partly explained in the name – Large Language Models. These models are neural networks with literally billions of parameters. That makes training the entire network extremely expensive, hence transfer learning methods are so commonly used. This allows specific applications to become more accurate using less data and fewer IT resources. Parameter-Efficient Fine-Tuning (PEFT) is an exemplary group of fine-tuning methods for open source LLMs, while OpenAI provides an API to do this inside their infrastructure.

Human Feedback

LLMs have seen major improvements in recent years as a result of reinforcement learning from human feedback. As LLM tasks are open ended, human feedback from end users is critical in evaluating the application’s performance and it allows engineers to make the necessary changes within their LLMOps pipeline. 

Performance Metrics

Traditionally, machine learning models have clearly defined performance metrics which are simple to calculate. LLMs, however, use a different set of standard metrics and scoring, such as bilingual evaluation understudy (BLEU) and Recall-Oriented Understudy for Gisting Evaluation (ROGUE). Nonetheless, the choice of the right metric for LLM is more demanding and will depend strongly on the type of task that you want to solve with it.


The vast majority of LLM applications focus on binding many external systems together with off-the-shelf LLM, rather than building a new LLM from scratch. That is why tools such as LangChain have become so popular. LangChain facilitates the process of building LLM-powered applications with their suite of tools, components and interfaces. An example of such integration can be a Q&A chatbot, backed by an external knowledge base kept in a vector database.

Looking to the future

The key benefits of MLOps and LLMOps are efficiency, scalability and risk reduction. Both principles allow data teams to achieve faster model and pipeline development, deliver higher quality models, and streamline deployment to production. They also enable vast scalability and management, as dozens of models can be overseen, controlled, managed, and monitored for continuous integration, delivery, and deployment. One can inherit most of the principles from MLOps to LLMOps. However, it is important to be aware of the underlying differences, which are typical for LLM applications but not so common for other machine learning applications. Today they can be quite challenging to tackle, but with the advancement and growing popularity of LLMs, these challenges become more manageable. 

As we look ahead, MLOps and LLMops will continue to grow in importance as more organisations consider scaling their AI efforts. Both models will help businesses automate their machine learning life cycles, improve the quality of their processes, and better leverage data to make decisions. Whilst it is unchartered territory for many companies, leveraging AI at scale will ensure businesses can derive the utmost value from their machine learning investments.