What is LLMOps, Why It Matters & Its 7 Best Practices in 2023

Large language model (LLM) applications are increasing after business users realized the language generation capabilities of GPT models like ChatGPT. Some of these benefits are reported as

• Increased productivity by 88% in software development
• Improved accuracy by 98% for fraud detection for financial institutions
• Reduced customer service costs by 30% in the insurance industry. ¹

These advantages leave companies with three choices:

• Develop a new foundation model
• Partner with an LLM provider to customize the proprietary model
• Fine-tune an existing model for related tasks

This decision changes based on the company budget, in-house talent, and the firm’s objectives and concerns. However, creating new foundation models costs 900 times larger than fine-tuning proprietary models and 9 times larger than third-party partnering.

LLMOps are a recent response to these high operational costs and lack of talent. Yet, the platforms that streamline large language model operations are not yet popular or clearly understood. In this article, we will explain LLMOPs inside-out by covering questions like what LLMOps are, why they are important, and the best tools available in the market.

What are LLMOps, and how do they work?

Large language models operations (LLMOps) are a specific type of MLOps that delivers necessary infrastructure and improves operational capabilities to facilitate deployment model training and optimization of language models and enable effective management of LLMs lifecycle, including:

• Training
• Fine-tuning
• Deployment
• Monitoring
• Maintenance.

Here is a video that explains what LLMOps are:

How is LLMOps different from MLOps?

LLMOps fall under the scope of MLOps, but it is specifically focused on streamlining LLM development. Therefore, some vendors call LLMOps as MLOPs for LLMs. However, there are some tasks 

• Transfer Learning: LLMs start with a foundation model and are fine-tuned with new data for domain-specific improvements, achieving peak performance with less data and resources.
• Human Feedback: Reinforcement learning from human feedback (RLHF) enhances LLM training, which is essential for evaluating performance due to the open-ended nature of LLM tasks.
• Hyperparameter Tuning: LLM hyperparameters impact accuracy and costs. Adjusting parameters like batch sizes and learning rates influences speed and expenditure.
• Performance Metrics: LLM evaluation introduces standards like BLEU and ROUGE, requiring careful implementation and interpretation.
• Prompt Engineering: Effective prompt templates are crucial for accurate LLM responses, mitigating risks such as model hallucination and data leakage.
• LLM Chains or Pipelines: LLM pipelines, facilitated by frameworks like LangChain, interlink multiple LLM calls or external system interactions, enabling complex tasks.
• Computational Resources: LLM training involves extensive calculations on large datasets. GPUs accelerate data-parallel operations, requiring access for efficient training and deployment. Model compression and distillation techniques manage inference costs. 

Why do we need LLMOPs?

While Large Language Models (LLMs) are great for easy prototyping, using them in real commercial products has its own challenges. Developing LLMs involves various complex stages like getting the right data, preparing it, crafting prompts, refining the model, deploying it, and keeping an eye on its performance. This requires different teams, from data experts to machine learning engineers, to work together smoothly.

To keep everything running smoothly, it’s crucial to have a structured approach, and that’s where LLMOps steps in. LLMOps help make experimenting, improving, deploying, and refining LLMs a well-organized and efficient journey, ensuring all the moving parts work together seamlessly.

For example, a travel agency needs a tool to generate personalized travel recommendations by understanding travel preferences and suggesting suitable destinations. This business can leverage transfer learning to fine-tune a powerful model like GPT-3 to suit its specific needs using domain-specific data, which includes user reviews, destination descriptions, and travel guides.

Figure 1 explains how foundational models are designed to reach desired model output by using transfer learning and the assistance of LLMOps:

Explore how enterprises build foundation models in our Enterprise Generative AI article and how to fine-tune LLMs.

What are the benefits of LLMOps?

LLMOps bring forth significant advantages, including:

1. Enhanced efficiency: LLMOps empower data teams to accelerate model and pipeline development, creating high-quality models and faster deployment in production settings.
2. Seamless scalability: With extensive scalability and management capabilities, LLMOps allow for overseeing and monitoring multiple models within a continuous integration, delivery, and deployment environment. Reproducible LLM pipelines foster collaboration, reduce conflicts, and hasten release cycles.
3. Reduced risk: LLMOps catalyzes transparency and swift responsiveness to regulatory requests, particularly as LLMs are often under regulatory scrutiny. This ensures better adherence to organizational or industry policies, enhancing risk management and mitigating potential challenges.
4. Integration with DataOps: LLMOps can seamlessly integrate with DataOps practices, facilitating a smooth data flow from ingestion to model deployment. This integration promotes data-driven decision-making and accelerates value delivery.
5. Faster iteration and feedback loop: Automating repetitive tasks and enabling rapid experimentation, LLMOps shorten iteration cycles and facilitate quick feedback loops. This agility is vital for adapting models to changing business needs.
6. Streamlined collaboration: LLMOps fosters collaboration among data scientists, ML engineers, DevOps, and stakeholders. By providing a unified platform for communication and insights sharing, it enhances model development and deployment.
7. Improved security and privacy: LLMOps prioritizes safeguarding sensitive information and data privacy, ensuring protection against vulnerabilities and unauthorized access.
8. Hyperparameter tuning: Through systematic adjustment of model configuration parameters, LLMOps fine-tunes hyperparameters like learning rates and batch sizes for optimal performance. Balancing exhaustive search and efficient exploration identifies parameter settings that lead to better model outcomes.
9. Better resource allocation: LLMOps ensures access to suitable hardware resources like GPUs for efficient fine-tuning while also monitoring and optimizing resource usage.
10. Enhanced performance: LLMOps directly contributes to improved model performance by ensuring high-quality and domain-relevant training data.

7 Best Practices for LLMOps

LLMOps can handle anything from data preparation to pipeline production. Here is a 7 steps to an effective LLMOps adoption: 

1.)Data management and security

Data plays a pivotal role in LLM training, underscoring the significance of LLMOps practices that predominantly emphasize robust data management and stringent security measures for sensitive data. Some of these are:

• Data storage: Choose suitable software to handle large data volumes, ensuring efficient data recovery across the LLM lifecycle.
• Data versioning: Maintain data changes and track development through data versioning.
• Data encryption and access controls: Protect data with transit encryption and implement access controls, like role-based access, to maintain secure data handling.
• Exploratory data analysis (EDA): Continuously prepare and explore data for the ML lifecycle, creating shareable visualizations and reproducible datasets.
• Prompt engineering: Craft reliable prompts to generate accurate queries from LLMs, promoting effective communication.

2.) Model Management

In LLMOps, efficient training, evaluation metrics, and management of LLM models take centre stage. These practices are meticulously designed to optimise model performance, data integrity adherence, and robust security measures throughout the model’s lifecycle. Here are some best practices:

• Selection of a foundation model: Choose a suitable pre-trained model as the starting point for customization, considering factors like performance, size, and compatibility.
• Few-Shot Prompting: Benefit from few-shot learning to quickly fine-tune models for specialized tasks without extensive training data, offering a versatile and efficient approach to leveraging large language models. 
• Model fine-tuning: Optimize model performance using widely recognized libraries and techniques for fine-tuning, enhancing the model’s capabilities in specific domains.
• Model inference and serving: Manage the model refresh cycle and ensure efficient inference request times while addressing production-related considerations during testing and quality assurance stages.
• Model monitoring with human feedback: Develop robust data and model monitoring pipelines with alerts for detecting model drift and identifying potential malicious user behaviour.
• Model evaluation and benchmarking: Establish robust data and model monitoring pipelines, incorporating alerts to identify model drift and potentially malicious user behaviour. This proactive approach enhances model reliability and security.

3.) Deployment

LLMOps can ensure seamless integration into the desired environment while optimizing model performance and accessibility by following these tips:

• Cloud-based and on-premises deployment: Select the correct deployment strategy by considering several aspects, such as budget, security, and infrastructure requirements. 
• Adapting existing models for specific tasks: LLMOps tailor a pre-trained model for specific tasks since it is cost-effective. This approach applies to customizing other machine learning models like natural language processing (NLP) or deep learning models. 

4.) Monitoring and maintenance

LLMops guarantee sustained performance and adaptability over time:

• Improve model performance: Establish tracking mechanisms for model and pipeline lineage and versions, ensuring efficient management of artefacts and transitions throughout their lifecycle. 

What is an LLMOps platform?

LLMOps tools are designed to streamline the development, deployment, and management of Large Language Models (LLMs). LLMOps platforms are categorized under three main groups: frameworks, platforms, and ancillary tools. 

Another categorization for LLMOps tools is whether they are open-source tools or proprietary. The choice between open-source and proprietary LLMOps tools depends on factors like customization, data security requirements, budget considerations, and the level of support needed.

Explore the best LLMOps platforms and learn which is most appropriate for you. 

What does LLM mean?

LLM stands for “Large Language Model,” referring to advanced AI models designed to understand and generate human-like language. These models are built with millions or even billions of parameters, enabling them to process and generate text with a high degree of complexity and nuance. LLMs have many applications, from natural language processing tasks to creative writing and problem-solving.

Further reading

Discover more on MLOps, AIOPs and LLMs by checking out:

If you have more questions, let us know:

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External sources

1. “The CEO’s Roadmap on Generative AI.” BCG. March 2023. Revisited August 11, 2023.
2. “LLMops.” Valohai. March 2023. Revisited August 11, 2023.