What Exactly are Large Language Model Operations (LLMOps)? What Exactly are Large Language Model Operations (LLMOps)?
Large language models (LLMs) are a powerful new technology with the potential to revolutionize many industries. However, LLMs are also complex... What Exactly are Large Language Model Operations (LLMOps)?

Large language models (LLMs) are a powerful new technology with the potential to revolutionize many industries. However, LLMs are also complex and challenging to manage. LLMOps, or Large Language Model Operations, is a new and emerging field that focuses on the operational management of LLMs. What goes into this new trend, what components do we need to pay attention to, and how do LLMOps benefit us?

Why do we need LLMOps?

Just as we needed a framework to develop machine learning models with MLOps, so too do we need an established framework for large language models. As Sahar Dolev-Blitental, VP of Marketing at Iguazio puts it, “Building your own Gen AI application and using it in your live business environment requires additional capabilities from your MLOps solution, and hence the importance of LLMOps.” 

Data Management

Data is the key ingredient for training effective LLMs. LLMOps teams need to ensure that they have access to high-quality, diverse, and representative data. This data needs to be cleaned and labeled in a way that is consistent with the desired task of the LLM. This is the best time to scour the data for any possible anomalies, areas where bias may be present, and any outliers. Be sure to clean your data before moving it into deployment.

For example, if an LLM is going to be used to generate marketing copy, then the training data should include a variety of marketing copy from different industries. The data should also be labeled with information about the target audience and the desired outcome of the marketing copy. While many LLMs are fairly broad, there are even emerging examples of domain-specific LLMs that would be more fine-tuned for your industry.

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Model Development

By the time you’ve collected and prepared your data, your team needs to decide on what model architecture you want to use and to train the model on your dataset(s). There’s no right answer for what model architecture to use, as it’ll depend on your team’s individual needs, what tasks you want to give it, and what your team can support. However, before you go into deployment, you need to be sure to test the performance of the model once it’s been trained and see where it needs to be improved.


Now that the model has been trained and evaluated, and all major bugs have been worked out, it’s ready for deployment into production for others to use. This may involve integrating the model directly with other software and systems as needed. This process may involve multiple teams, from the team who developed the LLM in the first place, to whoever’s going to be using it, to ensure that the model is performing as expected.


It is important to monitor the performance of LLMs in production to identify any potential problems. This stage includes monitoring for accuracy, response time, ethical concerns, and bias, and ensuring that there are no hallucinations.

LLMOps teams can use a variety of tools and techniques to monitor LLMs in production. For example, they can use logging and monitoring tools to track the performance of the model. They can also use human-in-the-loop evaluation to assess the quality of the model’s output.


Lastly, once the results seem accurate, timely, and with no hallucinations, it’s time to make sure that the results represent ethical and responsible standards set in place by your organization. Your team should have established clear guidelines early on the process, this is when you make sure everything aligns properly. No team wants to be responsible for deploying a model that has a racial or inaccurate bias! You can also use this time to amend any mistakes with the dataset that you may have overlooked, or fine-tune your model to account for any discrepancies in data.

Conclusion on LLMOps

We know that it’s still a bit early to define LLMOps as the field is seemingly barely a year old at this point. MLOps had years to be defined, so surely LLMOps will find its path over the next few years. In turn, it’s becoming important to keep up with any and all changes associated with LLMs. 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 LLM 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

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