LLMOps MLOPS

https://www.redhat.com/en/topics/ai/llmops

https://www.redhat.com/en/topics/cloud-computing/what-is-kubeflow

https://www.kubeflow.org/docs/started/architecture/

https://github.com/kserve/kserve

Large Language Model Operations (LLMOps) are operational methods used to manage large language models. With LLMOps, the lifecycle of LLMs are managed and automated, from fine-tuning to maintenance, helping developers and teams deploy, monitor, and maintain LLMs.

LLMOps vs. MLOps

If LLMs are a subset of ML models, then LLMOps is a large language model equivalent to machine learning operations (MLOps). MLOps is a set of workflow practices aiming to streamline the process of deploying and maintaining ML models. MLOps seeks to establish a continuous evolution for integrating ML models into software development processes. Similarly, LLMOps seeks to continuously experiment, iterate, deploy and improve the LLM development and deployment lifecycle.

While LLMOps and MLOps have similarities, there are also differences. A few include:

Learning: Traditional ML models are usually created or trained from scratch, but LLMs start from a foundation model and are fine-tuned with data to improve task performance.

Tuning: For LLMs, fine-tuning improves performance and increases accuracy, making the model more knowledgeable about a specific subject. Prompt tuning enables LLMs to perform better on specific tasks. Hyperparameter tuning is also a difference. In traditional ML, tuning focuses on improving accuracy. With LLMs, tuning is important for accuracy as well as reducing cost and the amount of power required for training. Both model types benefit from the tuning process, but with different emphases. Lastly, it's important to mention retrieval-augmented generation (RAG), the process of using external knowledge to ensure accurate and specific facts are collected by the LLM to produce better responses.

Feedback: Reinforcement learning from human feedback (RLHF) is an improvement in training LLMs. Feedback from humans is critical to a LLM’s performance. LLMs use feedback to evaluate for accuracy, whereas traditional ML models use specific metrics for accuracy.

Performance metrics: ML models have precise performance metrics, but LLMs have a different set of metrics, like bilingual evaluation understudy (BLEU) and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) which require more complex evaluation.

Benefits of LLMOps

With LLMOps becoming the best way to monitor and enhance the performance, there are three primary benefits to discuss:

Efficiency: LLMOps allows teams to develop models faster, improve model quality, and quickly deploy. With a more streamlined approach to management, teams can collaborate better on a platform that promotes communication, development and deployment.

Scalability: LLMOps aids in scalability and management because more than 1 model can be managed and monitored for continuous integration and continuous delivery/deployment (CI/CD). LLMOps also provides a more responsive user experience through improved data communication and response.

Risk reduction: LLMOps promotes more transparency and establishes better compliance with organization and industry policies. LLMOps can improve security and privacy by protecting sensitive information and preventing exposure to risks.