Model Lifecycles in the AI Era: LLMOps vs MLOps

By Patrick Ortiz | Trace3 Innovation

The Rise of a New Model Lifecycle
In the past few years, the world of machine learning (ML) has developed rapidly. With a new era in the data scene coming into focus, analytical and predictive models emerged to meet new business needs that hadn’t been tackled before. These models trained on enterprise data have proven to be invaluable, leading to key insights and wider boosts in business performance. As demand for enterprise-grade machine learning skyrocketed, a new solution emerged: MLOps. The set of practices and tools were designed to streamline the ML lifecycle holistically, from development to deployment. Born out of the intersection of data engineering, machine learning, and DevOps, it was aimed at making life easier for everyone.

Although enterprises have been quick to realize the benefits of implementing MLOps, development in the space continued on at an even more rapid pace. As soon as the market was settling down, a new player entered the game Large Language Models (LLMs). Focusing on the generation of complex content from data sets rather than prediction and analytics, the market became far more complicated to tackle. Because of the nature of these two differing forms of machine learning, end use cases have also shifted drastically. LLMs are applied widely across varying domains downstream whereas traditional ML models tend to be more specific to a single use case. These changes meant trouble in the Ops market as many of the widely used MLOps solutions have had trouble finding a way to cover the new needs and requirements of LLM operations (LLMOps).
As the LLM and Generative AI technology continue to reshape the ML landscape, organizations will be keen to quickly adapt and begin developing their strategies for adoption. Its at this point that it’s becoming clear to enterprises that a new set of practices are needed to create, deploy, and monitor these specialized models effectively.

MLOps meets LLMOps
Upon first glance you may be wondering why your current set of MLOps practices may not meet the needs of LLMs, especially when considering many of the lifecycle phases remain consistent across both domains. But when you dig a bit deeper into the intricacies of LLMs, the differences are even greater than one might have expected. What exactly are some of these key areas that differ between the two disciplines you should be taking into consideration?

Data Processing and Management
To begin with, the amount of data required to develop an LLM is far greater than that of standard ML models, requiring anywhere from 70 million to 16 billion parameters when starting from scratch. A large emphasis in both Ops practices is the need for data preparation including the sourcing, cleaning, and labeling of the information being used to train the models. Because of the size of data sets used for LLM training, the labeling is better handled through automation techniques or solutions specific to data annotation. In addition, data pre-processing for LLMs requires unique methods to achieve accuracy later in the lifecycle, these range from tokenization of text to quality filtering. Another consideration on the data processing side of LLMOps is the storage aspect of the data in the form of embeddings. To provide greater accuracy in the LLM, storing memory for the model to pull from requires implementation of specialized data stores for retrieval augmented generation.
Fine Tuning, and Prompt Engineering
An area of further distinction between MLOps and LLMOps is the lack of feature engineering. Originally this component of the ML lifecycle was geared towards model performance improvement and fine-tuning models to specific capabilities or tasks. With LLMs this process becomes quite different and can be done with a range of methods from few shot learning to parameter efficient fine tuning of the model.

Additionally, LLMs may require a process known as prompt engineering where inputs to the models are designed to elicit specific responses from the LLM.

Monitoring and Security
The end point of the operational lifecycle diverges from the standard ML monitoring and response due to the complicated nature of output evaluation and wider organizational use. For one the LLM requires tracking of model performance at a level that goes beyond standard prediction quality monitoring.
Model performance must be tracked across varied tasks and domains requiring constant output evaluation of the accuracy, bias, and ethics for a specific organization. Due to the sensitive data involved and the size of LLMs used in production, the security challenges shift drastically from that of a standard ML model. This ranges from the need to implement access control mechanisms and encryption, to the real-time auditing of LLM usage. In addition, new vulnerabilities and attack vectors exist that an organization may need to deal with as LLM implementation occurs.

Stepping into the Future of LLMOps
After taking a deeper look into some of the comparative practices between MLOps and LLMOps its clear that they cannot be handled efficiently by the same solutions or sets of practices. In fact, many emerging solutions in the AI/ML space are tackling these phases of the LLM lifecycle with both depth and efficiency in ways that traditional ML lifecycle tools cannot handle. However, if you want some steps to begin taking advantage of these emerging technologies here’s some pathways to consider:
  1. Ensure your org has comprehensive data management at the core (i.e., steady flows of high- quality data, automated collection and processing, versioning and control guardrails). Take a look here to find out more: Things to Consider For Data Collection and Preprocessing for LLMs (

  2. Develop out specific use cases and discern business areas that may benefit from implementation of this level of tooling. Here’s some ideas to take a look at: Four practical LLMOps use-cases | Cyces

  3. Begin internal evaluations of whether the teams currently in place at your organization have the capability and capacity to take on the larger scale project.

  4. Finally begin taking a look into the architecture and tooling to get a better picture of the current market offerings and where your organizations specific needs will lie. This article breaks down some of the open-source tools and frameworks you can start looking into: What is LLMOps? Architecture, Recommended tools (

For the moment, it may be wise for an enterprise to consider adopting fully developed applications and pre-trained models until the end-to-end development lifecycle is closer to an enterprise “ready” option. As time progresses and more organizations can take on LLM development or implementation the integration of best practices will be even more critical for streamlining the processes within their
organization. For now, the focus should be placed on finetuning foundation models for an easier ride to your ROI.
Additional Reading
LLMOps Emerging Solutions:

Development and Training

Deployment and Production

Monitoring and Management
Ortiz, Patrick - Bio Headshot 3Patrick Ortiz joined the Innovation team at Trace3 in May 2023 as a summer intern and quickly showcased success in content development and insight discovery. With a background in science and engineering research and a passion for understanding the latest trends across the enterprise IT space, he continues to bring in a forward outlook and deliver on content to help clients understand the ever-changing landscape of IT solutions. He will be completing his bachelor’s degree at Arizona State University and will join the Innovation team full-time. When not in classes or researching, Patrick can be found eating at some of the best foodie locations in whichever city he may be exploring next. 
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