In the rush to deploy Large Language Models (LLMs), companies often focus on the glamorous parts—the impressive capabilities, the transformative potential, and the competitive edge.
But beneath the surface lies a complex web of operational challenges that can undermine your model’s performance.
If you’re going to use LLMs in your business, you need a set of strategies, tools, and processing in place to keep your LLM running smoothly—you need Large Language Model Operations (LLMOps). Which in itself can cause problems if you’re not careful.
In this article, we’ll identify often-overlooked obstacles with LLMOps, and more importantly, explore actionable solutions to keep your LLMs effective and reliable.
Understanding LLMOps and Its Importance
From SaaS to Retail, businesses across industries have embraced the advancements of AI. Some are baking LLMs right into their products, others are using it to make internal operations a breeze, and many are using it to enhance and expand their customer support.
For a business to effectively deploy and manage those LLMs, they need LLMOps.
ℹ️ LLMOps stands for Large Language Model Operations.
You can think of it as a toolkit that helps companies manage the vast data requirements, ethical challenges, and performance demands of LLMs.
An example of LLMOps in action could be managing a large language model like GPT-4 within a customer service application, where the model is tasked with responding to customer inquiries.
Here’s how LLMOps would be applied, documented, and used in this context:
Setting Up Data Management and Documentation
- Gather and label customer inquiry data, such as categorizing inquiries by topic (e.g., billing, technical support).
- Record the data sources, preprocessing steps (like anonymization for privacy), and labeling criteria.
- Maintain a data versioning log to track updates or changes to the data, using tools like DVC (Data Version Control).
Fine-Tuning the Model
- Fine-tune the pre-trained LLM with the customer data to ensure it’s well-suited to handle the specific language and tone of inquiries.
- Document the model’s training configuration, including hyperparameters, dataset version, and evaluation metrics. This allows for reproducibility and helps track model changes over time.
Deployment and Inference Optimization
- Set up the model to run on a cloud-based service with optimized hardware for low-latency response times. Techniques like model quantization and caching frequently asked questions can improve efficiency.
- Outline the deployment architecture, scaling strategy, and optimization techniques used.
- Record latency benchmarks and include a runbook for scaling resources during high traffic.
Monitoring and Maintenance
- Implement automated model monitoring to detect changes in model performance, such as response accuracy or latency. Regularly audit responses for signs of bias.
- Create monitoring logs and a dashboard tracking key metrics (e.g., accuracy, response time, data drift). Then log incidents where the model underperforms and document corrective actions.
User Feedback and Iterative Improvement
- Collect feedback from customer service agents and customers to refine responses. Incorporate this data into future fine-tuning.
- Maintain a feedback tracker, document changes based on feedback, and update the model’s training data and performance metrics accordingly.
Without LLMOps, your data wouldn’t be clean enough to use, the LLM wouldn’t be personalized to your use case, and the solution might not scale as your needs do.
And why spend time and resources building an LLM-based solution if it only solves them for a short time?
So ultimately, LLMOps will make your LLM solution work and work for longer.
📚Want to learn more about LLMs? Check out our free resource: LLM Use Cases: One Large Language Model vs Multiple Models
Wondering how LLMOps differs from traditional machine learning operations? Check out this clip from our Talking AI podcast, where we discuss the key differences and why they matter.
The Hidden Challenges in LLMOps and How to Overcome Them
We’d love to say LLMOps is as easy as 1-2-3, but it’s more like debugging in production—doable but not without complexity. Not all of your challenges will be immediately obvious. Here’s what to expect: Let’s dig into each challenge and cover a solution or two to address them.Challenge 1: The Data Quality Dilemma
Adapting an LLM to your use case takes a bit of data engineering. You need high-quality, diverse data that’s specific to your use case—whether that’s customer support, industry-specific insights, or internal documentation.
However, sourcing relevant, unbiased data can be challenging. Pre-trained models are only as good as the data they’re refined with, and using skewed or narrow data can lead to biased, inconsistent responses.
Data annotation also plays a significant role here. Fine-tuning models require labeled data specific to the business context, like categorizing queries by department or tagging legal versus technical content.
This process can be time-consuming and may require expert insight, especially in specialized domains.
👉 Solution: Curating Relevant, High-Quality Data Efficiently
Start by gathering data that closely reflects the type of interactions your model will handle. For instance, if you’re using an LLM to support customer service, prioritize data like customer chat logs, support tickets, or FAQs.
Be proactive about cleaning out irrelevant or skewed data, like outdated responses or low-quality content from unreliable sources.
This process ensures the model can fine-tune itself on high-quality examples, helping it respond more accurately in real scenarios.
If your dataset lacks diversity or doesn’t cover specific situations, synthetic data can help.
For example, if you need more examples of customer feedback in different languages, you can use translation models or data augmentation techniques to expand your dataset without starting from scratch.
This way, you broaden the model’s understanding without extensive manual data collection.
For tasks that require labeled data, like tagging common query types, automated labeling tools can make the process faster.
Tools such as Amazon SageMaker Ground Truth or Labelbox can be used for data science and automatically tag repetitive patterns, reducing the need for manual input. For complex or varied data, consider crowdsourcing through platforms like Amazon Mechanical Turk or Scale AI.
This approach adds diverse perspectives, which can be especially useful if your model needs to serve a wide-ranging audience.
There’s also the option to use Exploratory Data Analysis or EDA. It can uncover patterns, spot anomalies, and check assumptions within your data. This will help detect noisy, irrelevant, or biased data, which can quietly undermine model performance if left unchecked.
Challenge 2: The Speed vs. Resource Trade-off
Using an LLM for specific business cases is expensive—no ifs, ands, or buts about it.
The computational demands can make your cloud bill look like a phone number, and that’s before you factor in the need for real-time responses.
When users expect snappy interactions, every millisecond of latency matters. A model that takes five seconds to respond might as well be broken in today’s fast-paced business environment.
👉 Solution: Boosting Efficiency with Smart Optimizations
First, you can solve this challenge by using optimization algorithms which can make model training and fine-tuning processes faster.
For instance, gradient checkpointing is a technique that reduces memory usage by selectively storing intermediate results, making it possible to train larger models without maxing out resources.
Other methods, like mixed-precision training, balance speed and accuracy by using lower-precision calculations where possible—enabling faster results without major performance trade-offs.
Then from there, GPUs and TPUs will be your best friend.
Specialized hardware, like GPUs (Graphics Processing Units) and TPUs (Tensor Processing Units), is designed to handle the massive computations LLMs require. For example, cloud providers like Google Cloud and AWS offer these accelerators, allowing companies to process models faster than with CPUs alone.
Newer advances, such as NVIDIA’s A100 GPU, push the limits of what’s possible, handling complex models with improved efficiency. Using these accelerators can cut down response times, bringing real-time applications closer to the speed users expect.
Challenge 3: Scaling Without Stumbling
Companies often face a choice between cloud-based or on-premises setups. While on-premises solutions offer control over data security, they come with high upfront costs and maintenance demands.
Cloud platforms, on the other hand, provide flexibility and scalability, making them ideal for handling the intensive workloads of LLMs.
Scaling LLMs for high traffic is another challenge. As user loads increase, so does the need for seamless scaling. But with large models, scaling isn’t straightforward. Techniques like model parallelism—splitting the model across multiple devices—and sharding—dividing the model’s data processing tasks—are often required to efficiently distribute the load.
👉 Solution: Flexible and Scalable Deployment Strategies
If you want to be flexible in your LLMOps deployment, you’ll need to adopt containerization and microservices.
Tools like Docker and Kubernetes allow you to containerize the model, packaging it with all dependencies for consistent deployment.
Kubernetes can then manage these containers across different servers, scaling them as demand fluctuates. This microservices architecture keeps each part of the application modular, making updates and scaling more manageable.
Flexibility isn’t your only solution, you’ll also need to be scalable.
Cloud providers like AWS, Google Cloud, and Azure offer specialized AI services that simplify model deployment. These platforms provide the necessary hardware, such as GPUs or TPUs, and auto-scaling options to match workload demands.
By leveraging the cloud, companies can scale quickly without investing in costly infrastructure, keeping the model responsive even during peak times.
Challenge 4: Walking the Tightrope of Compliance and Ethics
When you use an LLM, you’ll face both ethical concerns and compliance issues.
Data privacy regulations like GDPR demand strict controls on how personal information is collected, stored, and used. This is especially true for sectors like healthcare or finance, where they require careful oversight to avoid privacy violations.
And of course, LLMs often reflect biases in the training data, which can unintentionally lead to discriminatory outputs. This poses serious ethical risks, especially if the model is used in public-facing applications, as fairness and inclusivity are critical in today’s AI landscape.
👉 Solution: Building Ethical and Compliant AI
Detecting and reducing bias begins with diverse training data that represents a wide range of perspectives.
Techniques like adversarial training—where models are tested against challenging data—and fairness audits help detect biases early. Regularly assessing model outputs for fairness also promotes ethical AI use.
Compliance tools, like Microsoft’s Presidio for anonymizing sensitive data, help streamline privacy protection and simplify audits. Involving legal teams early in the AI development process helps organizations stay aligned with evolving regulations and reduce risks, ensuring that ethical practices are embedded from the start.
Challenge 5: Monitoring and Maintenance
Once your LLM is up and running, you’ll still need to pay close attention. We call this model management.
Model management includes tracking metrics like response accuracy, latency, and user satisfaction. Without ongoing monitoring, issues can go undetected, leading to suboptimal user experiences or even harmful outputs.
Model drift—where a model’s effectiveness decreases over time due to changes in real-world data—is a common challenge. Regularly updating and retraining the model with new data can prevent this, keeping responses accurate and up-to-date.
👉 Solution: Setting Up Robust Monitoring and Maintenance
For effective model review, you’ll want to use tools like Prometheus and Grafana. They offer real-time tracking and alert teams to performance dips as soon as they occur. You can set up specific alerts for key metrics you want to monitor. That way any unexpected changes can prompt quick investigation and adjustments. You’ll also want to plan regular updates with new, relevant data to prevent model drift. Tools like MLflow support version control for models, making it easy to track changes and revert if issues arise. Keeping a version history also helps maintain transparency, providing a clear record of how and when the model was last updated. Another way to prevent model drift is by using Retrieval Augmented Generation (RAG). RAG combines the generation abilities of LLMs with real-time, context-aware information retrieval. By accessing up-to-date knowledge stored in vector databases, an LLM can ground its responses in specific, relevant data, improving accuracy and relevance. We have loads of RAG resources for you to tap into. Check them out below:Case Studies: LLMOps in Action
Anyone successfully using an LLM in their company is, in some way, using LLMOps. Here are two real-life examples of it in practice—they just may inspire your own use.
Implementing LLMOps in Enterprise Settings
Kayo and HatchWorks AI: Delivering Real-Time Fleet Insights with LLMOps
Cox2M, a leading provider of commercial IoT solutions, partnered with HatchWorks AI to develop the Kayo AI Assistant for their fleet management customers. The assistant leverages Retrieval-Augmented Generation (RAG) to provide real-time trip analysis and fleet metrics. By securely integrating a large language model with their fleet data, Cox2M enabled fleet managers to access complex data using natural language queries.
HatchWorks AI’s Gen AI Innovation Workshop played a pivotal role in this transformation. “The workshop transformed how we think about Gen AI by getting our entire team on the same page and speaking the same language. It was the jumpstart we needed to help us identify and start building proofs of concept for Gen AI use cases across our business,” says Matthew Shorts, Chief Product & Technology Officer at Cox2M.
How LLMOps Was Applied:
- Data Management and Security: Handling vast amounts of fleet data—such as mileage, hard braking events, and trip start/end times—required robust data management practices. LLMOps ensured that this data was processed, stored, and retrieved efficiently and securely.
- Model Deployment and Infrastructure: HatchWorks AI collaborated with Cox2M to design a scalable and cost-effective cloud infrastructure using platforms like Google Cloud and Vertex AI. This facilitated the seamless deployment of the LLM within their existing systems.
- Natural Language Processing and User Experience: The Kayo AI Assistant was designed with a user-friendly interface, allowing fleet managers to interact using natural language. This simplified access to critical insights and enhanced usability.
- Continuous Improvement and Scalability: Built to be flexible, the assistant allows for continuous expansion of functionality as new use cases arise. LLMOps practices facilitated iterative development and deployment, ensuring the solution could scale with customer needs.
Outcomes and Benefits:
- Real-Time Insights: Customers gained easy access to real-time fleet metrics without prolonged development cycles, saving time and resources.
- Operational Efficiency: The assistant’s ability to provide accurate, real-time responses led to significant operational efficiencies for Cox2M’s clients.
- Scalability and Flexibility: The cost-effective and flexible infrastructure ensures scalability across different cloud environments, making it easier to attract new customers while retaining existing ones.
- Enhanced Customer Experience: By enabling natural language interactions, Cox2M improved the overall customer experience, making complex data more accessible and actionable.
Frequently Asked Questions (FAQs)
How can organizations overcome data preparation challenges?
Most organizations stumble because they’re fighting fires instead of building firewalls.
Data issues usually stem from inconsistent input standards, siloed teams, and manual cleaning processes that can’t scale. The solution starts with automated validation checks and standardized data pipelines.
What are the best practices for optimizing model performance?
Start with automated cleaning pipelines that catch common issues like duplicates, missing values, and format inconsistencies. Then implement data quality gates that prevent bad data from entering your systems in the first place. Think prevention, not just cure.How do ethical considerations impact LLMOps?
The big three issues are bias in responses, potential misuse (like generating harmful content), and data privacy.
Your LLM might inadvertently discriminate against certain groups, leak sensitive information, or be used in ways you hadn’t anticipated. Think of it like launching a powerful tool that needs careful guardrails.
