In the quickly advancing world of generative AI, two acronyms commonly appear--LLMOps and MLOps. With just one letter separating the two, it is understandable that many may wonder what sets them apart. This guide aims to explain LLMOps vs. MLOps, including their key differences and distinct applications.
What is LLMOps?
LLMOps, or Large Language Model Operations, is a subcategory of Machine Learning Operations (MLOps) in the worlds of machine learning (ML) and artificial intelligence (AI) that has emerged to address the unique challenges posed by training, operationalizing, deploying, and managing large language models (LLMs) such as OpenAI’s Chat GPT, Google’s BARD, and more.
LLMs are advanced AI models trained on vast amounts of data that use deep learning algorithms to learn information and generate human-like responses. These functionalities require vast amounts of computing power to generate, summarize, rewrite, search, classify, and cluster content data. LLMOps streamline the process of making LLMs so they can become more efficient, adaptable, and scalable.
What is MLOps?
MLOps is a well-established process that is crucial to the ML lifecycle. It encapsulates all of the practices and tools necessary to develop, deploy, and manage traditional ML models in production environments. Through automation, MLOps facilitates activities such as data preparation, model training, testing, deployment, and continuous monitoring so that ML models can sustain optimal performance and adapt to evolving data and production requirements.
Key Differences Between LLMOps vs. MLOps
Now that both concepts have been discussed individually, let's take a closer look at the differences between MLOps and LLMOps.
Data Management
In MLOps, high volumes of data are required to train a model from scratch, and lesser volumes of data are needed to fine-tune pre-trained models. LLMOps builds upon MLOps data management process in both volume and sophistication. LLMs require high-quality, extremely diverse, and vast data sets. However, not all of the data is used at once. Prompt engineering techniques like zero- and few-shot prompts encourage the model to hand-pick samples and carefully curate data for processing.
Model Experimentation
In MLOps, experiments are run throughout the development process to determine the best-performing data configurations for model development. In contrast, LLMOps can use raw data and domain-specific data sets.
Model Evaluation
In MLOps, the model is evaluated through an assessment of accuracy, precision, recall, and other performance standards through techniques such as cross-validation, baseline model comparison, and more. On the other hand, LLMOps evaluates models using both intrinsic metrics like ROUGE, BERT, and BLEU and expert or crowdsourced human evaluation of specific outputs.
Cost
The cost of MLOps is largely dependent on the expense of data collection and preparation, experimentation resources, feature engineering, and hyperparameter tuning. In contrast, the cost of LLMOps is largely influenced by the price of LLMs, many of which are proprietary.
Latency
MLOps may encounter latency issues that stem from the size of the model, the complexity of the computations, the limitations of hardware, the qualities of the network, and more. LLMOps may experience even more latency-related challenges due to the immense size and complexity of LLMs and their computations.
When To Use MLOps vs. LLMOps
Ultimately, the choice between MLOps vs. LLMOps is determined by the nature of the project. To make a decision, consider the type of ML model involved and the requirements of the project.
If the project deals with broad applications of ML, MLOps is most appropriate. This includes models with simple architectures, support vector machines, and neural networks for specific applications like spam filtering, image recognition, and recommendation systems.
On the other hand, if the project involves LLMs and massive amounts of data, LLMOps is required. Additionally, LLMOps offers more specialized tools and techniques and cutting-edge technology that is necessary for advanced ML models with high computational demands.
Keep in mind LLMOps is a specific type of MLOps and takes MLOps many steps further by adding practices specific to LLMs, driving the future of ML and AI.
Encora’s MLOps and LLMOps
When it comes to MLOps and LLMOps, it is crucial to partner with an expert who amplifies innovation, creativity, and efficiency through the disciplined application of generative AI tools and methods.
Fast-growing tech companies partner with Encora to outsource product development and drive growth. We are deeply expert in the various disciplines, tools, and technologies that power the emerging economy, and this is one of the primary reasons that clients choose Encora over the many strategic alternatives that they have.
Contact us to learn more about LLMOps and MLOps.
