What is LLMOps?

Recent progress in Large Language Models (LLMs), such as OpenAI’s GPT, Google’s Bard, and Databricks’ Dolly, is fueling substantial expansion within enterprises. Consequently, there exists a demand for optimal strategies to operationalize these models. LLMOps facilitates the efficient deployment, monitoring, and upkeep of large language models. This process necessitates collaboration among data scientists, DevOps engineers, and technology professionals, mirroring the principles of traditional Machine Learning Ops (MLOps).

LLMOps is a fusion of "LLM" (Large Language Models) and "MLOps" (Machine Learning Operations).

• LLMs, as foundational models, exhibit versatile capabilities in performing various NLP tasks, including text generation, classification, conversational question answering, and translation.
• MLOps is a field that aims to optimize and automate the entire lifecycle of machine learning models. It serves as the underpinning for LLMOps.
• LLMOps employs MLOps principles and infrastructure tailored to LLMs, positioning itself as a subset within the broader MLOps framework.

Here are several differences between LLMOps from MLOps:

Computational resources. LLMs require specialized hardware like GPUs for efficient pre-training and fine-tuning due to extensive calculations on large datasets. Access to these resources is crucial, and model compression techniques (e.g., pruning or quantization) are essential to manage inference costs.

Transfer learning. Unlike traditional ML models, LLMs often begin with a foundation model and undergo fine-tuning with new data. This approach optimizes performance in specific domains, requiring fewer data and compute resources.

Human feedback. Reinforcement Learning from Human Feedback (RLHF) enhances LLM training. Given the open-ended nature of LLM tasks, integrating end-user feedback into LLMOps pipelines is vital for evaluation and future fine-tuning.

Hyperparameter tuning. Tuning hyperparameters is not only about improving accuracy but also reducing training and inference costs for LLMs. Adjusting parameters like batch sizes and learning rates significantly impacts speed and cost of training.

Performance metrics. LLMs use different metrics (e.g., BLEU and ROUGE) than traditional ML models. Implementing and understanding these metrics is crucial for accurate evaluation.

Prompt engineering. Effective prompt templates play a vital role in ensuring accurate responses and mitigating risks like model hallucination and prompt hacking.

LLM chains or pipelines. Building LLM pipelines using tools like LangChain or LlamaIndex connects multiple LLM calls or external system calls. Development efforts often concentrate on creating these pipelines for complex tasks rather than building new LLMs.

Uncover the crucial best practices tailored to each stage of LLMOps principles. Whether you're at the exploratory stage, fine-tuning models, or ensuring governance and compliance, these best practices serve as a guide to optimize your LLMOps journey. 

Exploratory Data Analysis (EDA). Explore, share, and prepare data iteratively for the ML lifecycle, ensuring reproducibility and shareability.

Data prep and prompt engineering: Iteratively transform aggregate, de-duplicate data, and develop structured prompts for reliable queries, ensuring team visibility and shareability.

Model fine-tuning. Utilize open-source libraries like Hugging Face Transformers, DeepSpeed, PyTorch, TensorFlow, and JAX for fine-tuning and enhancing model performance.

Model review and governance. Track model and pipeline lineage, manage artifacts through their lifecycle and collaborate across ML models using open-source MLOps platform such as MLflow or WandB.

Model inference and serving. QA and testing should manage model refresh frequency, inference request times, and production specifics. Automate the preproduction pipeline using CI/CD tools and enable REST API model endpoints with GPU acceleration.