LLMs are changing the way we build and maintain AI-powered products. This will lead to new methods and guidelines for the lifecycle of LLM-powered applications. The term LLMOps stands for Large Language Model Operations which is a new set of tools and best practices to manage the lifecycle of LLM-powered applications, including robust development, deployment, and maintenance. The short definition is that LLMOps is MLOps for LLMs, so to make LLMOps compatible with ML Dev Tooling, it is important to integrate these techniques with existing ML workflows and tools. This will help ensure that the additional capabilities required for LLMOps are incorporated into the overall workflow and that LLMs can be effectively deployed and monitored in production.
- Addressing the ambiguity of natural languages: LLMs sometimes output inconsistencies that must be consistently tracked and corrected. This also includes ensuring model fairness and avoiding biases.
- Managing costs and latency: While individual calls to an API may not be expensive, the costs can add up quickly as the number of calls increases, making it expensive to use an MLOps API. Hosting an MLOps system can also be costly as it requires heavy infrastructure. Fine-tuning can also be challenging if the needed hardware is unavailable.
- Data management, Scalability and Query performance of vector databases: Vector databases require careful management of data to ensure that the vectors accurately represent the underlying data points. As the amount of data stored in a vector database grows, it can become challenging to scale the database to handle the increased load
- Integrating with existing tools and workflows: One important consideration is how well the LLMOps tool integrates with existing tools and workflows, such as data sources, data engineering platforms, code repositories, CI/CD pipelines, monitoring systems.
LLMOps best practices
An effective LLMOps strategy can ensure optimal language model performance, scalability, and efficiency, enabling organisations to unlock their full potential and gain a competitive edge. Best practices in LLMOps include :
- Prompt engineering: it involves crafting prompts that guide the model towards producing the desired output. Keeping track of the different prompts and their performance will lead to easy, quick and reproducible experimentations which will improve the communication between the members of a team.
- Vector database : a type of database that stores data in the form of vectors (or embeddings in the context of NLP). It can help improve the performance and accuracy of LLMs by providing them with relevant context to guide their responses.
- CI / CD : an effective CI/CD system can help improve the performance and reliability of LLMs by ensuring that they are always up-to-date with the latest changes and improvements. It will also ensure that pipelines in production are updated quickly and reliably.
- Fine Tuning LLMs : it includes making the data format suitable for the application, keeping track of the hyperparameters, using appropriate evaluation metrics and being mindful of potential biases.
- LLM deployment : it involves setting up the infrastructure to run LLMs, including selecting the appropriate hardware resources and designing an architecture that can effectively handle large volumes of data and support real-time applications.
- LLM observability : monitoring the performance of LLMs and troubleshooting issues as they arise. A good practice would be to detect any potential problem and be able to quickly fix it and update the pipeline.
The following figure presents an example of an LLMOps architecture that shows where some of the best practices fit into the LLM lifecycle.
- Prompt Engineering and the Vector database as a critical part of the data pipeline.
- LLM deployment and observability as orchestrated tasks.
- Model selection, validation, hyperparameters as tracked experiments.
New tools are spawning everyday in the rising field of LLMOps. The following list is a set of the best tools that were available to date. In order to keep up with the latest tools we suggest to constantly have a look at this github repository that is constantly updated regarding the newest and latest tools : https://github.com/tensorchord/awesome-llmops.
- Open source LLMs: MPT, Falcon, Llama.
- Modelling: Langchain, Langflow, LlamaIndex.
- Experiment tracking: ClearGPT, WandB prompts.
- Evaluation: Langkit, PromptFoo.
- Fine tuning techniques : PEFT, QLoRA.
- Monitoring : Grafana, Prometheus.
In conclusion, LLMOps is an emerging field that focuses on managing large language models. Some best practices include prompt engineering, deploying LLMs, and monitoring. There are several challenges associated with implementing LLMOps, including addressing the ambiguity of natural languages and managing costs and latency. Several tools are available to help teams monitor the performance of LLMs in production.
As the field matures, LLMOps is expected to continue to evolve and grow as more organizations adopt large language models and seek to optimize their performance. As the field matures, we can expect to see the development of new tools and techniques to help teams effectively manage their LLMs and unlock their full potential.
At Alleycorp Nord, we have been working on generic LLMOps solutions that are easy to tailor for a variety of projects (all projects are different!). Your project relies on LLMs and you think you would benefit from more automated tools? We would be happy to chat, contact us!