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Idan Novogroder
Idan Novogroder Author

Idan has an extensive background in software and DevOps engineering....

Last updated on June 7, 2024

Large Language Models (LLMs) are pretty straightforward to use when you’re prototyping. However, incorporating an LLM into a commercial product is an altogether different story. The LLM development lifecycle is made up of several complex components, including data intake, data preparation, engineering, model fine-tuning, model deployment, model monitoring, and more.

The process also calls for smooth communication and handoffs among teams ranging from data engineering to data science to ML engineering. To keep all of these processes synchronized and operating together, strong operational practice is key.

This is where LLMOps comes in. It’s an operations approach for the experimentation, iteration, deployment, and continuous improvement phases of the LLM development lifecycle.

Keep reading to learn what LLMOps is all about, see how it differs from MLOps, and learn a few best practices for the smooth delivery of an LLM-powered app.

What is Large Language Model Operations (LLMOps)?

LLMOps stands for Large Language Model Operations and refers to the specialized methods and processes meant to accelerate model creation, deployment, and administration over its entire lifespan. 

These processes include data preparation, language model training, monitoring, fine-tuning, and deployment. LLMOps, like Machine Learning Ops (MLOps), is based on cooperation among data scientists, DevOps engineers, and other IT teams.

The current LLMOps landscape consists of:

  • Large Language Models – we wouldn’t be talking about LLMOps if LLMs didn’t first appear on the scene.
  • LLM-as-a–Service – providing the LLM as an API through their infrastructure, the most common way to deliver closed-based models.
  • Custom LLM stack – a larger range of tools used to fine-tune and implement proprietary solutions based on open-source principles.
  • Prompt engineering technologies – they enable in-context learning rather than fine-tuning, which is less expensive and doesn’t require using sensitive data.
  • Vector databases – a vector database extracts contextually appropriate data for certain commands.
  • Prompt execution tools – they optimize and improve model output by managing prompt templates and creating chain-like sequences of pertinent prompts.

LLMOps vs MLOps

LLMOps could be interpreted as MLOps upgraded with processes and technologies that address the unique requirements of LLMs. Key considerations include:


LLMOps generates costs around inference, while standard MLOps cost data collection and model training. Although costly APIs during experimentation may incur costs, protracted prompts incur inference costs.

Computational resources

Training and fine-tuning big language models often require massive levels of calculations on massive datasets. To accelerate this process, you need specialized hardware, such as GPUs, which have become critical for training and deploying big language models.

Transfer learning 

Unlike many standard ML models that are built or trained from scratch, many LLM models begin with a foundation model and are fine-tuned with fresh data to increase performance in a given domain. Fine-tuning enables cutting-edge performance for specific applications with less data and fewer computational resources.

Human feedback

Reinforcement learning from human feedback (RLHF) has led to significant advances in big language model training. Since LLM operations are frequently open-ended, human feedback from end users is frequently required for evaluating LLM performance. Integrating such feedback loops into your LLMOps pipelines facilitates assessment while also providing data for future fine-tuning of your LLM.

Hyperparameter adjustment

In traditional ML, hyperparameter tuning is often focused on increasing accuracy or other metrics. Tuning is especially important for LLMs as it reduces the cost and compute resources required for training and inference. 

For example, changing batch sizes and learning rates can significantly change the pace and cost of training. So, both traditional ML models and LLMs get to benefit from tracking and optimizing the tuning process.

Performance metrics 

Traditional ML models feature well-defined performance measures, such as accuracy, AUC, and F1 score. These indicators are relatively easy to calculate. When it comes to evaluating LLMs, however, a whole separate set of standard metrics and scoring apply. 

Examples include bilingual evaluation understudy (BLEU) and Recall-Oriented Understudy for Gisting Evaluation (ROUGE), which call for some extra care

Prompt engineering

Instruction-following models can manage more complicated prompts or sets of instructions. Engineering prompts are crucial for receiving correct and consistent replies from LLMs. Prompt engineering can lower the risk of model hallucination and prompt hacking, such as injection, data leakage, and jailbreaking.

LLM chains or pipelines

LLM pipelines created with tools such as LangChain or LlamaIndex, combine several LLM calls and/or calls to other systems like vector databases or web searches. These pipelines enable LLMs to do sophisticated activities such as knowledge base Q&A or addressing user inquiries based on a collection of documents. In reality, LLM application development often concentrates on creating pipelines rather than creating new LLMs.

How Does LLMOps Work?

MLOps and LLMOps have similar steps. Foundation models alter the stages of constructing an LLM-powered app, while pre-trained LLMs are adapted to downstream tasks instead of being trained from the start.

Here are a few key steps in the LLMOps process:

1. Foundation model selection

You can use foundation models – LLMs pre-trained on huge data sets – for many downstream operations. Few teams out there have the resources required to train a foundation model from scratch, which is hard, time-consuming, and expensive. A 2020 Lambda Labs study found that training OpenAI’s GPT-3 with 175 billion parameters would take 355 years and $4.6 million on a Tesla V100 cloud instance.

Teams can choose between proprietary or open-source foundation models depending on performance, cost, simplicity of use, and flexibility.

Companies with huge expert teams and AI budgets can develop proprietary foundation models, which perform better than open-source models and are larger. The biggest drawback of proprietary models is their pricey APIs and lower adaptability with closed-source foundation architectures.

Proprietary model vendors include OpenAI (GPT-3, GPT-4), AI21 Labs (Jurassic-2), and Anthropic (Claude). HuggingFace hosts open-source models as a community hub, but they may be smaller and less capable than proprietary versions. However, they are more cost-efficient and flexible than proprietary models.

Examples of open-source models:

  • Stable Diffusion
  • LLaMA 
  • Flan-T5
  • GPT-J, GPT-Neo and Pythia

2. Downstream task adaptation

After selecting your foundation model, you’re ready to use the LLM API. Note that LLM APIs might be confusing since they don’t always state what input causes what output. The API returns a text completion for any text prompt, trying to match your pattern.

How do you make an LLM provide the desired output? Model accuracy and hallucinations are definitely issues to consider. Getting the LLM API output in your preferred format may take iterations, and LLMs might hallucinate without the right data.

To address these issues, teams can adapt foundation models to downstream activities such as:

  • Prompt Engineering
  • Fine-tuning pre-trained models
  • Using external data to provide contextual information
  • Using embeddings
  • Model assessment

In MLOps, you validate ML models on a hold-out validation set with a performance metric. But is this method equally good for evaluating LLM performance? Some recent approaches are model A/B testing or using LLM-specific evaluation technologies like HoneyHive and HumanLoop.

3. Model deployment and monitoring

LLM deployment varies greatly across versions, so LLM-powered apps must keep a close eye on API model changes. LLM monitoring tools like Whylabs and HumanLoop exist for this purpose.

LLMOps Benefits

LLMOps can improve efficiency for a broad range of challenges by supporting:

  • Vector databases to get contextually relevant information.
  • Data collection
  • Data preparation, and timely engineering from a diverse range of sources, disciplines, and languages.
  • produce writing, like document code or procedures, and translate languages.
  • Data labeling and annotation are combined with human input to provide complicated, domain-specific judgment.
  • Data storage, categorization, and versioning with storage technologies to facilitate retrieval, and modification across the LLM lifetime.
  • Exploratory data analysis (EDA) to examine, prepare, and share particular data for the machine learning model lifetime.
  • Model inference and serving. 
  • GPU acceleration for REST API model endpoints.
  • Model review and governance allow you to track model and pipeline versions and control their entire lifespan. This allows for cooperation amongst ML models. Model monitoring, including human input, for your LLM applications. 
  • Identify possible malicious assaults, model drift, and opportunities for improvement.
  • Prompt analytics, logging, and testing.
  • Prompt engineering, including tools for in-context learning rather than fine-tuning with sensitive data.

Benefits of LLMOps

Efficiency, performance, and speed

LLMOps helps your teams perform more with less in a multitude of ways, starting with team collaboration. Data scientists, ML engineers, DevOps, and stakeholders may interact more swiftly on a unified platform for communication and insight sharing, model creation, and deployment, resulting in speedier delivery. 

Optimizing model training, picking the right architectures, and using methods like model pruning and quantization can all lower computational costs. LLMOps can assist in secure access to appropriate hardware resources, such as GPUs, allowing for effective fine-tuning, monitoring, and resource optimization.

Furthermore, LLMOps simplifies data management by promoting solid data management standards that help to guarantee high-quality datasets are sourced, cleaned, and used for training.

Hyperparameters like learning rates and batch sizes can be modified to achieve peak performance, while integration with DataOps can promote a seamless data flow from intake to model deployment – and enable data-driven decision-making. 

You can speed up iteration and feedback cycles by automating monotonous operations and allowing for rapid experimentation. LLMOps can use model management to simplify the creation, training, evaluation, and deployment of large language models, ensuring that they are optimized. 

High-quality, domain-relevant training data can help models perform better. Additionally, by continually checking and updating models, LLMOps ensures top performance. Model and pipeline development may be hastened to generate higher-quality models and get LLMs into production sooner.

Risk reduction

You can increase security and privacy by prioritizing sensitive information protection with advanced, enterprise-grade LLMOps, therefore reducing vulnerabilities and unwanted access. Transparency and prompt replies to regulatory demands promote better compliance with your organization’s or industry’s regulations. 


LLMOps make it simpler to scale and manage data, which is critical when thousands of models must be supervised, controlled, maintained, and monitored for continuous integration, continuous delivery, and continuous deployment. LLMOps may do this by optimizing model latency, resulting in a more responsive user experience.

Scalability can be improved by including model monitoring in a continuous integration, delivery, and deployment environment. LLM pipelines may promote cooperation, eliminate disagreement, and accelerate release cycles. The repeatability of LLM pipelines allows for more tightly tied collaboration across data teams, decreasing conflict with DevOps and IT while increasing release velocity.

LLMOps can manage massive numbers of requests continuously, which is very important for business applications. The approach also improves workload management, even when the workloads in question tend to fluctuate.

LLMOps Components

The key components of LLMops include:

Architectural design and selection

This includes tasks such as:

  • Selecting the right model architecture – This involves issue domain, data, computing resources, and model performance.
  • Customizing models for tasks – You can use pre-trained models and customize them to save time and money. There are tools to fine-tune NLP models for text categorization, sentiment analysis, and named entity identification.
  • Optimization of hyperparameters – Tuning hyperparameters optimizes model performance by finding the best combination. Grid search, random search, and Bayesian optimization are typical methods. 
  • Preparation and tweaking – Transfer learning and unsupervised pre-training minimize training time and increase model performance.
  • Benchmarking and model assessment – Depending on the job, accuracy, F1-score, or BLEU are used to evaluate model performance. Benchmarking models against industry standards is another good practice. GLUE and SuperGLUE provide standardized datasets and activities to measure model performance across domains.

Data management

This part consists of tasks such as:

  • Data gathering and processing – LLMs run on high-quality, diverse training data, so your model will likely require data from several sources, domains, and languages. Before feeding into the LLM, noisy, unstructured data must be cleaned and preprocessed. 
  • Labeling and annotating data – Supervised learning requires reliable and consistent labeled data. Annotating data using human specialists ensures quality. Complex, domain-specific, or ambiguous instances requiring expert judgment benefit from human-in-the-loop techniques. Teams can quickly and cost-effectively acquire large-scale annotations on Amazon Mechanical Turk.
  • Store, organize, and version data – Data storage, retrieval, and modification during the LLM lifecycle are easier with the right database and storage solutions that can handle the scale.
  • Data version control – Datasets and models should be versioned using data version control technologies, as this allows for smooth transitions between various dataset versions. This lets AI teams collaborate and reproduce experiments using data version control systems. LLM iteration and performance improvement are easier with a clear data history. Versioning models and testing thoroughly helps discover errors early, ensuring only good models are distributed.
  • Data privacy and protection – this includes anonymization and pseudonymization techniques, model security considerations, data access control, and compliance with data protection regulations like GDPR and CCPA.

Deployment strategies and platforms

This area involves the following tasks:

  • On-premises vs. cloud deployment – The optimal deployment approach relies on funding, data security, and infrastructure. Cloud implementations are flexible, scalable, and easy to use. On-premises implementations may improve data security and control. 
  • Model maintenance – Make sure to monitor model performance and usage to discover flaws or issues like model drift.
  • Optimizing scalability and performance – In high-traffic settings, models may need to be scaled horizontally (more instances) or vertically (additional resources).

Ethics and Fairness

Ethics and fairness are critical components in the creation and implementation of large language models. Addressing biases in data and model outputs, adopting fairness-aware algorithms, and following AI ethics standards may all contribute to more responsible and transparent AI systems. 

Make sure to engage different stakeholders in AI decision-making. Focus on accessibility and inclusion to build AI systems for users with varying abilities and guarantee linguistic and cultural representation.

The scope of LLMOps in machine learning projects can be as specific or broad as the project requires. In some circumstances, LLMOps might cover everything from data preparation to pipeline production, but in others, only the model deployment procedure has to be implemented. 

LLMOps Best Practices

Here are some tips to help your operations run more smoothly.

Exploratory Data Analysis (EDA)

Exploratory data analysis (EDA) involves iteratively exploring, sharing, and preparing data for the ML lifecycle. The idea here is to produce repeatable, editable, and shareable data sets, tables, as well as visualizations.

Data prep and prompt engineering

Data preparation and prompt engineering include iteratively transforming, aggregating, and de-duplicating data before making it accessible and shareable across data teams. This opens the door to the iterative creation of prompts for organized, trustworthy inquiries to LLMs.

Model fine-tuning

You can use popular open-source libraries like Hugging Face Transformers, DeepSpeed, PyTorch, TensorFlow, and JAX to fine-tune and increase model performance.

Model review and governance

Another best practice is to track the provenance and versions of models and pipelines, as well as manage the artifacts and transitions throughout their lifecycle. Using an open-source MLOps platform like MLflow, you can discover, share, and collaborate on several ML models.

Model inference and serving

Manage the frequency of model refresh, inference request times, and other production-specific details in testing and QA. To automate the preproduction workflow, use CI/CD solutions like repositories and orchestrators. Also, enable REST API model endpoints with GPU acceleration.

Model monitoring with human feedback

It’s smart to build model and data monitoring pipelines that include alarms for both model drift and harmful user activity.

Be part of the community

Engage with the open-source community to stay up to date on the latest breakthroughs and best practices. Things are moving fast in the world of LLMs!

Smart resource management

LLM training and inference require significant computations on huge datasets. Specialized machines equipped with GPUs can speed data-parallel processing and many other processes. But they come with a high price tag, so it’s essential that you develop cost-saving practices before jumping on the LLM bandwagon.

Continuous model monitoring and maintenance

Monitoring methods help to discover shifts in model performance over time. Real-world input on model outputs is what you need to improve and retrain the model. Make sure to implement tracking tools for model and pipeline lineage, as well as versions, to guarantee that artifacts and transitions are managed efficiently throughout their existence.

Data management

Another important point is selecting appropriate software for managing big data volumes and ensuring efficient data recovery throughout the LLM lifespan. Data versioning allows you to track changes and developments in your data.

Encrypt data in transit and use access restrictions to protect it. Automate data gathering, cleansing, and preparation to ensure a consistent flow of high-quality information. And make sure that datasets are versioned to provide smooth transitions between different dataset versions.

Ethical issues

Ethical model building includes anticipating, discovering, and correcting biases in training data and model outputs that may affect results.

Privacy and compliance

Conduct frequent compliance checks to ensure that operations comply with legislation such as GDPR and CCPA. With AI and LLMs in the spotlight, you may see more scrutiny.

What is an LLMOps Platform?

An LLMOps platform gives data scientists and software engineers a place to work together that lets them explore data iteratively, work together in real time to track experiments, do quick engineering, manage models and pipelines, and control model transitioning, deployment, and monitoring for LLMs.

LLMOps automates the operational, synchronization, and monitoring phases of the machine learning lifecycle.

The Future of LLMOps

A key trend shaping the future of LLMOps is, well, AI itself. AIOps systems are intended to automate and improve LLMOps procedures. They employ artificial intelligence and machine learning to monitor LLMs, fix issues, and find areas for improvement.

One of the most notable advances in LLMOps is the proliferation of cloud-based LLMOps systems. Cloud-based LLMOps systems offer a highly scalable and elastic environment for installing and managing LLMs. They also provide a number of features and services that may be used to automate and optimize LLMOps activities.

Another rising concept in LLMOps is edge computing. Edge computing can be used to bring LLMs closer to the end user, improving latency and lowering bandwidth costs. Edge computing is also suitable for real-time applications like natural language processing and customer support.

Federated learning is also a potential new approach for training LLMs while respecting privacy. Federated learning enables LLMs to be trained on data spread across several enterprises without having to share information with one another to address data privacy concerns while maximizing the potential of massive databases.


LLMOps are critical for firms that seek to use the potential of LLMs. LLMOps teams keep up with the most recent developments and advancements in LLMOps, as well as implement proactive tactics to solve new difficulties. Working together, LLMOps teams may help shape the future of LLM management and increase their effect on society.

LLMs are growing more powerful and intelligent, and LLMOps teams are devising new and inventive methods for managing and maintaining them. Organizations are on their way to adopting current trends like LLMOps to capture the value of AI – and isn’t likely to stop anytime soon.

Ready to test out your LLMOps skills? Check out this guide on using lakeFS + LangChain AutoLoader and build reproducible LLM-based applications at scale.

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