LLMOps—Benefits, Implementation, and Best Practices

Large language models (LLMs) are revolutionizing numerous industries, but their immense potential hinges on effective operationalization. LLMOps offers a specialized toolkit for deploying, monitoring, and maintaining LLMs in production environments.

This article examines why LLMOps is needed beyond MLOps for enterprise AI adoption and explores the LLMOps process, tools, and best practices throughout the LLM lifecycle.

Summary of key LLMOps concepts

What is LLMOps and why do we need it?

Role of LLMOps in building generative AI applications

Large Language Model Operations(LLMOps) includes the practices, techniques, and tools used to manage the entire operational life cycle of large language models (LLMs) from selection to production. Data scientists, engineers, and IT teams use LLMOps to deploy, monitor, and maintain LLMs efficiently. Analogous to MLOps (Machine Learning Operations) but tailored for the nuances of LLMs, LLMOps ensures LLMs deliver consistent and reliable results.

Difference between LLMOps and MLOps

MLOps and LLMOps are derived from DevOps and have the same goal: enhancing efficiency and quality using automation throughout the AI/ML development cycle. 

Classic MLOps helps you build apps for your ML use cases.  It addresses a broader range of model architectures, with less emphasis on massive data pre-processing or frequent fine-tuning cycles.

However, generative AI use cases require extending MLOps capabilities to meet more complex operational requirements. That’s where LLMOps becomes essential. It provides additional mechanisms for managing LLM customization (and the required data pipelines) along with the LLM testing and monitoring requirements.

Need for LLMOps beyond MLOps

Here’s a closer look at some key pain points addressed by LLMOps:

Experimentation and reproducibility

Manually replicating LLM tuning experiments is prone to errors and inconsistencies. It becomes challenging to compare and evaluate different LLM configurations effectively. LLMOps automates the pipeline, capturing all parameters for consistent experimental conditions and facilitating comparisons of different LLM configurations.

LLM monitoring and maintenance

Monitoring LLM performance, diagnosing issues, and identifying root causes can be time-consuming and difficult. LLMOps facilitates comprehensive monitoring, providing real-time insights into LLM behavior and resource utilization. It also provides mechanisms to track LLM datasets and code changes, reducing errors and increasing collaboration.

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Safety and security 

LLM security concerns are complex because of the LLM’s ability to process natural language. The LLM model itself, its interconnected systems, and the actions of developers and users can all create security failure points. They face new challenges like prompt-based attacks and training data poisoning. LLMOps technologies address these concerns more effectively than MLOps.

Interpretability

In MLOps, model explainability is crucial for debugging and understanding model behavior. For instance, simpler models used in computer vision allow engineers to visualize which parts of an image contribute most to the model’s classification decision. This level of interpretability facilitates troubleshooting and performance optimization.

In contrast, LLM behavior is more like a black box, and introducing explainability is more challenging. LLMOps practices include explainability mechanisms that reveal the input data elements most influential in shaping the LLM’s output. They also support human evaluation and feedback of LLM output.

LLMOps in the LLM lifecycle

How LLMOps extends DevOps

LLMOps takes a structured approach to managing the entire LLM lifecycle, from development, tuning, and deployment to ongoing monitoring. It ensures efficient and reliable operation of LLMs in production. Key stages of the LLM lifecycle are given below.

Exploratory data analysis

The process begins with data exploration and understanding of the data characteristics of your use case. This might involve creating data visualizations and identifying patterns or outliers. Next comes data collection, where information is gathered from various sources relevant to the LLM’s intended use case. Finally, the collected data is meticulously cleaned. This cleaning process removes errors, inconsistencies, and duplicate entries, ensuring a high-quality dataset is available for LLM tuning.

Model selection and customization

Choosing the appropriate LLM architecture based on the desired task and available computational resources. Popular choices include BERT for tasks involving understanding textual relationships or GPT for its general-purpose text generation and translation capabilities.

Applications can use the LLM as is, and with extensive prompt engineering, quality and acceptable results are possible. However, the vast majority of use cases benefit from model customization. Some model customization methods include:

Fine-tuning

This process fine-tunes the parameters of an LLM on a new dataset to adapt it for a specific task. For example, fine-tuning LLM BERT on a smaller dataset of movie reviews and adjusting its parameters to classify sentiments as positive or negative. To avoid overfitting, fine-tuning requires careful management of learning rates, batch sizes, and other training parameters.

Prompt tuning

Prompt tuning enhances the capabilities of LLMs by employing soft prompts—adjustable vectors that are optimized and integrated alongside input text to guide the model’s responses. The model’s pre-existing parameters are kept “frozen,” but examples show the expected input-output format. Do read prompt tuning vs. fine-tuning for more details on the topic.

Retrieval augmented generation(RAG)

In RAG, model parameters are not changed. Instead, you convert domain data to vector embeddings and index it in a vector database. When the user enters a query, your application performs a similarity search of the prompt embedding against the index. It feeds the resulting data as a context within the LLM prompt. Learn more about RAG in our in-depth article.

LLMOps technologies take a continuous tuning approach. Over time, you must refresh training datasets and update the parameters to create a new model version—even for deployed models. LLMOps sets up the pipeline for data pre-processing, model customization, and model evaluation to create newer model versions when needed.

Model deployment

Once customized, the LLM is deployed to production. This stage focuses on:

• Preparing the trained LLM model for efficient serving in a production environment. 
• Setting up the resources required to run the LLM model in production.
• Integrating the deployed LLM with the applications or services that utilize its capabilities.

LLMOps includes technologies that continually deploy the application infrastructure and model(s) into the specified environments after evaluating the models’ performance with metric-based evaluation or humans in the loop. A typical pattern consists of deploying modes first in a quality assurance(QA) stage, testing them, and then manually approving deployment from QA to production.

Monitoring

LLMOps doesn’t stop at deployment – it continuously monitors the LLM’s performance and behavior. This stage involves:

• Monitoring key metrics like accuracy, latency (response time), cost, and fairness ensures the LLM functions as expected.
• Identifying deviations from typical behavior that could signal potential issues or performance degradation.

Monitoring should send alerts in case of a drop in output quality or data drift.

LLMOps tools and implementation

Several different tools can be used to implement LLMOps in your organization. A complete list of open-source LLMOps tools can be found here. We give some popular examples below.

Data engineering

As discussed above, any LLM project begins with data engineering. You have to select and prepare data sets relevant to your use case for further LLM customization. Data engineering for LLMOps comprises two main tasks.

1. Data ingestion—where data is collected or imported into a central repository or data lake from various sources.
2. Data transformation—where unprocessed data is cleaned, enriched, and structured into a usable format.

Nexla data engineering platform is an all-in-one solution for multi-speed data integration, preparation, monitoring, and discovery. You can build data flows to and from any system at any speed. For LLMOps, you can ingest data from any system, such as APIs, databases, files, and streams, and connect to any vector database. You can make data transformation workflows by applying filters, aggregations, joins, etc., through visual tools or custom code. Nexla provides connectors and adapters to handle different data formats (such as CSV, JSON, and XML) and protocols (such as REST and FTP). You can also create pipelines to automate the process so your information always remains updated.

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LLM customization and deployment

Once your data is ready, you can begin the model selection and customization process. There are several tools you can use for LLM customization. For example, Haystack lets you quickly compose applications with LLM Agents, semantic search, question-answering, and more. Treescale is an all-in-one development platform for LLM apps. You can deploy LLM-enhanced APIs seamlessly using tools for semantic querying, prompt optimization, statistical evaluation, version management, and performance tracking. However, among the several on the market, LangChain is more popular. 

LangChain is a set of tools that supports every stage of the LLM lifecycle. Chains are the fundamental principle that holds various AI components together. A chain is a series of automated actions from the user’s query to the model’s output.

With LangChain, you can:

• Build your applications using open-source building blocks, components, third-party integrations, and templates.
• Inspect, monitor, and evaluate your chains with LangSmith so you can continuously optimize and deploy confidently.
• Deploy any chain into an API with LangServe.

Consider the code below. It sets up a pipeline for processing natural language using the LangChain library. It initializes components such as a language model from OpenAI, defines a conversation prompt template, and creates a processing chain. The input question and context are provided, triggering the pipeline to generate a response that summarizes the given input text.

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

import os
from dotenv import load_dotenv

load_dotenv()

os.environ["OPENAI_API_KEY"]=os.getenv("OPENAI_API_KEY")
os.environ["LANGCHAIN_TRACING_V2"]="true"
os.environ["LANGCHAIN_API_KEY"]=os.getenv("LANGCHAIN_API_KEY")

prompt=ChatPromptTemplate.from_messages(
    [
        ("system","You are a helpful assistant. Please resposne to the user request only based on the given context"),
        ("user","Question:{question}\nContext:{context}")
    ]
)
model=ChatOpenAI(model="gpt-3.5-turbo")
output_parser=StrOutputParser()

chain=prompt|model|output_parser
question="Can you summarize the speech?"
context="""Copy your speech here."""

print(chain.invoke({"question":question,"context":context}))

LLM observability

Once your LLM is deployed, you can use Langsmith for observability. LangSmith provides exceptional rendering and processing for LLM traces, including token counting (assuming token counts are unavailable from the model provider). 

A run is a span representing a single unit of work or operation within LangSmith This could be anything from a single call to an LLM or chain, to a prompt formatting call, to a runnable lambda invocation. You can check on runners through the LangSmith visual interface.

Run view in LangSmith

LLMOps best practices

Implement the below best practices for long-term success.

Emphasize data management and security

LLM capabilities depend on high-quality training data. Ensure robust data collection, pre-processing, and storage. During training, apply strict security practices to safeguard sensitive data. 

Select the appropriate model

Selecting an LLM requires careful consideration of its accuracy for your particular use case. Models like Claude 3 and GPT-4 show varied performances in different tasks. Model size and resource requirements also impact future costs. It is best to assess LLMs through benchmarks and competitive arenas. LMSYS Chatbot Arena is a crowdsourced open platform that ranks LLMs based on over 400,000 human preference votes. You can use it to gain insights into user preferences and model effectiveness in conversational contexts.

Implement model version control

Reproducibility, tracking changes, and modeling version control are fundamental in managing LLMs over time. Use LLMOps tools that support version control of your models for changes in weights, architecture, and preprocessing steps, among others. Ideally, the tool should support easy switching between model versions at run time for complete flexibility.

Deploy for scale and flexibility

Deploy the model in a way that will allow it to scale while remaining adaptable. Consider dockerizing, not using servers, or having small service-oriented architectures. Make sure you can integrate with existing systems and APIs without any hitches.

Ensure ongoing LLM performance

Watch out for biases when an LLM generates responses for real-world user input. Install tools that can detect changes in patterns within input datasets, which may arise as a need to retrain the model.

Conclusion

LLMOps helps you manage your entire LLM lifecycle with maximum productivity. It unifies AI development across your organization by adding structure and enforcing governance. You can encourage cross-functional collaboration by sharing models, data, and insights between teams. LLMOps tools and practices help your organization enhance AI maturity cost-effectively and practically.