LLMOps vs. MLOps: Understanding the Differences
Alexandra Quinn | February 8, 2024
Data engineers, data scientists and other data professional leaders have been racing to implement gen AI into their engineering efforts. But a successful deployment of LLMs has to go beyond prototyping, which is where LLMOps comes into play. LLMOps is MLOps for LLMs. It’s about ensuring rapid, streamlined, automated and ethical deployment of LLMs to production. This blog post delves into the concepts of LLMOps and MLOps, explaining how and when to use each one.
To read more about LLMOps and MLOps, checkout the O’Reilly book “Implementing MLOps in the Enterprise”, authored by Iguazio’s CTO and co-founder Yaron Haviv and by Noah Gift.
What is LLMOps?
LLMOps (Large Language Model Operations), is a specialized domain within the broader field of machine learning operations (MLOps). LLMOps focuses specifically on the operational aspects of large language models (LLMs). LLM examples include GPT, BERT, and similar advanced AI systems.
LLM models are large deep learning models that are trained on vast datasets, are adaptable to various tasks and specialize in NLP tasks. They are characterized by their enormous size, complexity, and the vast amount of data they process. These elements need to be taken into consideration when managing, streamlining and deploying LLMs in ML pipelines, hence the specialized discipline of LLMOps.
Addressing LLM risks is an important part of gen AI productization. These risks include bias, IP and privacy issues, toxicity, regulatory non-compliance, misuse and hallucination. Mitigation starts by ensuring the training data is reliable, trustworthy and adheres to ethical values.
How Does LLMOps Work?
LLMOps pipelines and processes include components we’re familiar with in MLOps (see below), but adapted for LLMs. For that reason, productizing gen AI extends beyond prototype development, and LLMOps tools address challenges like scale, performance and costs.
To conduct the process of moving from GenAI prototyping to production, ensure the following steps are accounted for:
- Prototype development.
- Building resilient, modular production pipelines.
- Continuous monitoring of resources, data, and metrics.
- Collecting feedback for further tuning.
To support these steps, incorporate the following four LLMOs architecture elements:
- 1. Data Pipeline - Manages and processes various data sources.
- 2. ML Pipeline - Focuses on training, validation and deployment.
- 3. Application Pipeline - Manages requests and data/model validations.
- 4. Multi-Stage Pipeline - Ensures correct model behavior and incorporates feedback loops.
With these set up, you can move to the key LLMOps activities:
- Data Handling and Management - The organization, storage and pre-processing of the vast data needed for training language models. This includes versioning, ingestion and ensuring data quality.
- Language Model Training - This step focuses on scalable and distributed methods for training extensive language models. It involves parallel processing, distributed computing and automating hyperparameter tuning.
- Deployment of Language Models - This is about putting the large language models into use, typically through APIs or as services. It requires setting up infrastructure, balancing loads, scaling and monitoring for a reliable and efficient operation.
- Ongoing Monitoring and Maintenance - Constantly observing the performance, health and resource utilization of models. This includes measuring metrics, spotting irregularities and setting up alerts for immediate action. Regular updates and retraining are often part of this stage as well.
- Security and Compliance - This is important for protecting the security and privacy of LLMs and their data. It includes implementing access controls, data encryption, adhering to legal requirements and considering ethical aspects like responsible AI.
- CI/CD - This involves using CI/CD practices to automate the testing, validation, and deployment of language models. This approach allows quicker iterations and minimizes the risk of errors when models are in use.
- Collaboration and Reproducibility - LLMOps emphasizes teamwork and the ability to replicate results in language model operations. This includes version control, tracking experiments and documentation to foster collaboration among data scientists, engineers and researchers.
Read more about implementing LLMOps in practice here.
What is MLOps?
MLOps (Machine Learning Operations) is the set of practices and processes of streamlining and optimizing the deployment, monitoring, and maintenance of ML models in production environments. This ensures that the models are effective, efficient, and scalable, so they can reach production in an efficient, cost-effective and timely manner.
MLOps is a merger of ML with DevOps practices to cover the entire lifecycle of the ML model, from development and testing to deployment and maintenance. Activities include managing data, selecting algorithms, training models, and evaluating their performance. This is done automatically, at scale, and while enhancing collaboration.
How Does MLOps Work?
The key stages and practices in the MLOps lifecycle include:
1. Data Collection and Preparation
- Accessing historical or online data from multiple sources
- Cataloging and organizing the data for efficient analysis
- Cleansing, imputing and converting to numerical/categorical values, or transforming from unstructured (text, JSON, image, audio) to structured formats
- Feature engineering and automations, preferably with feature stores
2. Building an Automated Model Development Pipeline
- Designing ML pipelines that can automatically collect and prepare data, select features, run training, evaluate models and run model and system tests. The pipeline should support versioning, logging and visualization.
- Implementing triggers for running the pipeline, like when code or packets change, drift is detected, or data changes
- Building the pipeline over microservices, implementing CI and ensuring scalability and security.
3. Building Online ML Services
- Integrating with business applications.
- Deploying into the production pipeline, which includes:
- Data collection, validation and feature engineering logic in real-time
- Model serving
- API services and logic
- Data, model and resource monitoring
- Logs for events, telemetry and data/features logging
- Ensuring the deployment flow: Developing production components, testing with simulated data, deploying and continuously monitoring for drift and needed retraining or upgrades
4. Continuous Monitoring, Governance, and Retraining
- Monitoring data and models for quality and drift
- Improving model accuracy using techniques like AutoML
- Addressing the liabilities associated with AI services.
Read more about MLOps considerations and practices here.
MLOps vs. LLMOps: Comparison Table
| MLOps | LLMOps | |
| Scope | Lifecycle management of ML models | MLOps for LLMs |
| Model Complexity | Varied, from simple to complex models | High complexity due to size and scope |
| Deployment Challenges | Requires overcoming silos, technological considerations and resource issues | MLOps challenges + model size, integration requirements and ethical AI considerations |
| Resource Management | Focus on efficient use of resources and automated scaling for scalability and cost-effectiveness | Emphasis on managing extremely large computational resources |
| Performance Monitoring | Continuous monitoring for accuracy, drift, etc. | Specialized monitoring for biases, ethical concerns, and language nuances |
| Model Training | Regular updates based on performance metrics and drift detection | Updates may involve significant retraining and data refinement |
| Ethical Considerations | Depending on the application, can be a concern | High priority due to the potential impact on communication and content generation |
What is the Future of LLMs and LLMOps?
2023 was the year of GenAI, and it doesn’t seem like the momentum is slowing down. What’s in store for LLMOps and how can data professionals prepare? Here are a few expected trends:
1. Transitioning from Prototyping to Business Value Creation - The engineering focus will shift from showcasing demos to actual productization of GenAI and deploying LLMs to support live use cases. Therefore, businesses need to invest in the full AI and ML lifecycle, ensuring considerations like risk management, cost-effectiveness, scalability and continuous operation.
2. Enhanced Focus on Accuracy - Accuracy will become a key factor in GenAI applications, since businesses can't afford inaccurate or inappropriate responses. The emphasis will be on fine-tuning models to align with brand voice and target audience, and extensive testing will be necessary to ensure reliability.
3. Specific Use Case Realization Over General Hype - Businesses will identify specific, practical use cases for GenAI, seeking to maximize ROI. This realization will encourage a strategic integration of AI technologies, moving beyond just generative AI as a marketing stunt to a more comprehensive AI strategy.
4. GenAI as a Productivity Enhancer - GenAI will be seen as a force multiplier for productivity, rather than a standalone solution. Its integration into existing processes can enhance various tasks, from code development to marketing and creating presentations.
5. Shift to Integrated Solutions - The trend will move from isolated niche GenAI tools to integrated solutions. This includes out-of-the-box, industry-specific SaaS offerings and embedding GenAI functionalities into existing software platforms. Larger corporations might dominate this space, overshadowing smaller entities.
Learn more about the future of LLMOps here.
How Does MLRun Help Bring Gen AI to Production?
MLRun is an open-source, MLOps orchestration platform that simplifies the process of managing and automating ML pipelines and LLM deployments. It provides a framework for building, deploying, monitoring, and managing ML applications in a scalable and efficient manner.
MLRun automates various stages of the ML lifecycle, such as data preparation, model training and deployment. It is designed to handle large-scale ML workloads and offers tools for monitoring ML models in production, while helping with tracking model performance and managing model updates. MLRun also supports collaboration by integrating with version control systems and facilitating collaborative workflows.
MLRun is an MLOps and LLMOps platform built to support deployment of LLMOps as well. It can manage the lifecycle of LLMs, from training to deployment, while optimizing computational resources for scale and monitoring for performance and accuracy.
