LLMOps vs MLOps: Understanding the Differences

Large language models are all the rage right now, and with that comes the need for better management, organization, and planning. Just as machine learning leads to MLOps, so too have LLMs led to LLMOps.

While LLMOps and MLOps have many similarities, such as ensuring data is clean and there are no major bugs with deployment, there are also some key differences between the two fields. Here, we examine the similarities and differences between MLOps and LLMOps with a focus on how the latter of the two is crucial to explore in the coming months.

Focus

LLMOps is specifically focused on the operational management of LLMs, while MLOps is focused on all machine learning models. This means that data scientists need to be specifically aware of the nuances of language models and text-based datasets, such as factoring in linguistics, context, domains, and the potential computational cost.

At the end of the day, while there are plenty of places where MLOps and LLMOps overlap, they’re entirely different subsects of data science, meaning different frameworks, tools, skillsets, and processes will be involved.

Challenges

Data scientists working on large language models face some unique challenges that are not present in traditional MLOps projects. For example, LLMs are often much larger than traditional datasets used for machine learning and thus are computationally expensive to train and evaluate. Because of that, teams need to be more aware when monitoring and evaluating these models for more potential issues of bias, hallucinations, and so on.

Sahar Dolev-Blitental, VP of Marketing at Iguazio pointed out “Deploying LLMs securely in user-facing applications is a new and complex challenge that renders MLOps more relevant than ever.” She continues, “The models are much bigger in size and require multiple GPUs to run, so optimization and quantization are important to reduce cost and increase latency. Unlike simpler models that can perform for some time without the need for retraining, LLMs require a feedback loop consisting of real-time validation, monitoring of responses, and RLHF (reinforcement learning from human feedback).”

Benefits

Traditional MLOps may be better than nothing for developing large language models, but the nuances of language models can benefit by paying attention to these unique considerations. LLMOps in particular can benefit from improved performance, reliability, and scalability, just as MLOps can. However, with LLMops, this can see improved performance of language-based outputs, better understanding of language context, and possibly decreased computational costs.

Maturity

LLMOps is a newer field than MLOps, and there are fewer mature tools and resources available for LLMOps teams. This means that LLMOps teams may need to develop their own tools and processes or rely on a mix of open-source and commercial solutions. This may involve a different set of skills that aren’t as established as those required for MLOps and leaves more questions to be answered. For example, where does prompt engineering fit into the pipeline?

Conclusion

While LLMOps is still in its infancy, there are clear ways that we can get started with them by taking cues from the already-established field of MLOps. Rather than using one versus the other, it’s just a matter of deciding what’s right for your organization, factoring in cost, resources, staff, and time. It’s now important to stay up-to-date with the evolving field of LLMs, especially as the world is now more focused on language models than ever. The best place to do this is at ODSC West 2023 this October 30th to November 2nd. With a full track devoted to NLP and LLMs, you’ll enjoy talks, sessions, events, and more that squarely focus on this fast-paced field.

Confirmed sessions include:

• Personalizing LLMs with a Feature Store
• Evaluation Techniques for Large Language Models
• Building an Expert Question/Answer Bot with Open Source Tools and LLMs
• Understanding the Landscape of Large Models
• Democratizing Fine-tuning of Open-Source Large Models with Joint Systems Optimization
• Building LLM-powered Knowledge Workers over Your Data with LlamaIndex
• General and Efficient Self-supervised Learning with data2vec
• Towards Explainable and Language-Agnostic LLMs
• Fine-tuning LLMs on Slack Messages
• Beyond Demos and Prototypes: How to Build Production-Ready Applications Using Open-Source LLMs
• Adopting Language Models Requires Risk Management — This is How
• Connecting Large Language Models — Common Pitfalls & Challenges
• A Background to LLMs and Intro to PaLM 2: A Smaller, Faster and More Capable LLM
• The English SDK for Apache Spark™
• Integrating Language Models for Automating Feature Engineering Ideation
• How to Deliver Contextually Accurate LLMs
• Retrieval Augmented Generation (RAG) 101: Building an Open-Source “ChatGPT for Your Data” with Llama 2, LangChain, and Pinecone
• Building Using Llama 2
• LLM Best Practises: Training, Fine-Tuning, and Cutting Edge Tricks from Research
• Hands-On AI Risk Management: Utilizing the NIST AI RMF and LLMs

What are you waiting for? Get your pass today!

Originally posted on OpenDataScience.com

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