Operationalizing Large Language Models: How LLMOps can help your LLM-based applications succeed

The recent strides made in the field of machine learning have given us an array of powerful language models and algorithms. These models offer tremendous potential but also bring a unique set of challenges when it comes to building large-scale ML projects. In this blog post we will discuss the importance of LLMOps principles and best practices, which will enable you to take your existing or new machine learning projects to the next level.

Naturally, training a machine learning model (regardless of the problem being solved or the particular model architecture that was chosen) is a key part of every ML project. Preceded by data analysis and feature engineering, a model is trained and ready to be productionized. But what happens next?

We may observe a growing awareness among machine learning and data science practitioners of the crucial role played by pre- and post-training activities. Assigning more weight to these parts is one of the key principles of LLMOps.

What is LLMOps?

To start simply, you could think of LLMOps (Large Language Model Operations) as a way to make machine learning work better in the real world over a long period of time. As previously mentioned: model training is only part of what machine learning teams deal with. Other steps include:

• data ingestion, validation and preprocessing,
• model deployment and versioning of model artifacts,
• live monitoring of large language models in a production environment,
• monitoring the quality of deployed models and potentially retraining them.

Of course, in the context of Large Language Models, we often talk about just fine tuning, few-shot learning or just prompt engineering instead of a full training procedure. Nevertheless, this also requires certain steps such as the preparation of prompt templates, evaluation of results etc. Moreover, however you decide to improve the accuracy of your models, you still need to make sure your system works reliably and consistently.

As a set of concepts, guidelines and tools, LLMOps helps with all of these steps by providing tools and practices that make it easier to manage and maintain a machine learning system. Those tools and practices not only help to integrate consecutive steps (see Figure 1) together and make them work smoothly; they also make sure that the whole process is reproducible, automated and properly monitored at each stage – model training as well as model inference. This approach is as important for fine-tuning Large Language Models internally as for projects where you only use LLM-as-a-service for the inference part (think of, e.g., using OpenAI API for calling GPT models). Even though you don’t train or own the model, you still should pay attention to what data comes in and out of it. One should carefully monitor the whole process, log the predictions and so on.

Now that we have a common understanding of the term itself, let’s delve into how adopting LLMOps principles will make your project more efficient, sustainable and robust.

Making sure your LLMs are healthy and produce the desired results

One of the most important concepts, the importance of which I cannot stress enough, is monitoring. It covers monitoring the quality of input and output data, overall responsiveness and behavior of models as well as observing the traffic that goes into your LLM-based application. Why are these elements so important?

You have probably already invested a great deal of time in choosing the right architecture for the application, the whole team has decided which large language model would be the best choice for your use case, the model has been fine-tuned on your knowledge base, you have all the pieces and you are ready to go into production. That’s great! However, what usually happens in such scenarios is that right after a model is deployed in production, teams are gradually reassigned to other projects, analyzing new data and training new models. But the application is still live, right?

A lot of things may go wrong from here, e.g.:

• your system began to receive much more traffic than you initially estimated and is not able to handle so many requests (see Figure 2), resulting in many errors being returned and users being unsatisfied with your product,
• even if you don’t have a problem with the stability of the solution, the response time may turn out to be unacceptable for end users, as they have to wait too long for the responses due to the large amount of traffic and bottlenecks in your application,
• production traffic is very inconsistent, resulting in huge peaks of user requests during which your system consumes a large number of resources (RAM, CPU or GPU utilization, as in Figure 2). For a certain period of time it may not cause any trouble, but with more and more traffic it may eventually lead to the services hosting your Large Language Models frequently crashing.

There are more challenges than those listed above, such as changes in model behavior due to a sudden drift of input data (see Figure 3). When users start to use your application differently than you expected, the predictions of Large Language Models can also change significantly. This can lead to incorrect results being presented to end users.

Implementing a proper monitoring platform, dashboards visualizing resource consumption and application health is essential. We cannot forget about other components that are part of modern applications that utilize Large Language Models, e.g., vector databases, frontend services – every part of a mature, production-grade system should be carefully monitored. Naturally this applies to every long-term LLM-based application, whether it is a chatbot, a simple knowledge retrieval service or any other use case.

Allowing engineers to do productive work instead of tedious tasks

Apart from monitoring, another key concept of LLMOps is the automation of software development (or machine learning) processes. In many projects, a lot of time and effort is assigned to exhausting and repetitive work such as:

• the manual deployment process, requiring engineers to go through a checklist of actions to be able to push a newer release of the application into production; even scarier is that in case a bug is found, such a process has to be manually reversed step by step,
• regularly checking the monitoring dashboard to see if the application works as expected, producing the desired results (as discussed in the previous section) etc.

The problem is that, despite being so monotonous, these tasks are at the same time very important for your application and also highly error-prone! Not the best combination, right?

What we can do about this problem is to automate these tasks (see Figure 4) to make sure they are executed frequently in the same manner, leaving room for human error and allowing developers to focus on other tasks at the same time, increasing your team’s productivity.

The manual deployment process can be replaced with an automated continuous delivery pipeline which takes care of testing your LLMs and application thoroughly. Developers may only be needed to finally approve the deployment process but not to execute a whole list of manual steps. Instead of regular, tedious checking of charts and values, you could configure a monitoring platform to send you a notification (it could be Slack, e-mail or other tools you use) whenever, e.g., a metric drops below a certain threshold or an application becomes unresponsive for a certain amount of time. Take a look at an example of such a setup presented in Figure 4. In such a setup there are only two manual actions where a human-in-the-loop is necessary while the rest happens without human intervention:

• a Data Scientist merges a change in the training code or parameters to a codebase. This triggers a bunch of quality checks (e.g. linting, tests) and starts a training job. During training, we log all the model metrics and metadata automatically. A successful run produces model artifacts saved in a model registry, ready to be picked up for inference,
• in the second part, the team validates model metrics and decides to deploy the model they have just trained. A Machine Learning Engineer approves a model in the registry and this action automatically triggers a validation procedure which, if successful, delivers a working deployment into an inference environment.

Of course, the desired level of automation is different for each project. It depends on the scale, the frequency of deployments, and the overall maturity of the machine learning system. But this example should give you an overview of how properly applied automation processes can reduce the risk of human error and make the whole end-to-end process much faster and more reliable.

Reducing the overall cost of the project and application

Last but not least, implementing an LLMOps platform can eventually lead to significant savings in your project budget. Let’s discuss this further with an example of the aspects we described earlier (i.e., monitoring and automation).

First of all, thanks to monitoring, you will know the exact utilization of your infrastructure. If your LLM-based service is deployed in a cloud environment, for example, you can adjust the type of instance you are using to avoid unnecessary costs for resources you do not consume. On the other hand, if you use external models as an API (such as OpenAI GPT), it may be a good idea to track your expenses using a billing dashboard provided by the vendor or to build an in-house system to observe the costs and their correlation with, e.g., the length of input being sent to these models.

Observing such metrics will allow you to compare different architectural choices, cloud providers or specific Large Language Models that you deploy. Monitoring is crucial, not only for the sake of keeping your project healthy but also for optimizing business metrics and expenditure.

On the other hand, as we already mentioned, automation can free up your team from working on monotonous tasks on a daily basis and will allow you to utilize their knowledge and time in a much more productive way.

There are also other LLMOps concepts or practices that we did not explicitly mention in this blog post, such as model retraining and automatic data labeling. More than that, applications that utilize Large Language Models often include other key components such as vector databases – exactly the same principles (of monitoring, automation etc.) apply to these parts of your system.

All of these, when implemented correctly, can help you maintain your project in a more robust and cost-effective way.

Conclusion

Having LLMOps (or MLOps, in the context of smaller language models) best practices implemented in your machine learning system makes it more reliable, robust and automated. Once such standard practices and automation are implemented, delivering new models into production will require nothing but a few commands or clicks, as the rest of the process will happen behind the scenes. You will be able to track every log, metric and behavior, and thanks to the monitoring stack you will be able to react to events or unforeseen behaviors quickly.

And once again, keep in mind that using models-as-a-service through third party APIs from OpenAI or other platforms does not mean that you do not need to apply LLMOps principles anymore. In fact you only outsource the training and model-serving parts of the project and while it may not be your concern for short-term MVP projects, you should definitely look at LLMOps-related practices whenever you plan to build a serious machine learning-based product.