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How do LLMs work and what are they used for in companies?

Before we discuss LLMOps, let鈥檚 first explain what large language models are. They are machine learning systems that have been trained on huge collections of text-from books to web articles to source code, but also images and even video. As a result, they learn to understand the grammar, semantics, and context of human language. They use the transformer architecture first described by Google researchers in 2017 in the article 鈥淎ttention Is All You Need鈥� (https://arxiv.org/pdf/1706.03762v5.pdf). This allows them to predict the next words in a sentence, creating fluent and natural language.

As versatile tools, LLMs in companies are widely used for, among other things:

• building internal vector databases for efficient retrieval of relevant information based on understanding the query, not just keywords鈥� an example might be a law firm that uses LLM to create a vector database of all relevant laws and court rulings. This allows for quick retrieval of information key to a particular case,
• automating CI processes/CD (Continuous Integration/Continuous Deployment) by generating scripts and documentation 鈥� large technology companies can use LLMs to automatically generate code, unit tests and document new software features, speeding up release cycles,
• collection, preparation and labeling of data 鈥� LLM can help process and categorize massive amounts of text, image or audio data, which is essential for training other machine learning models.

Companies can also match pre-trained LLMs to their industries by teaching them specialized language and business context (fine-tuning).

However, content creation, language translation, and code development are the most common uses of LLMs in the enterprise. In fact, LLMs can create consistent product descriptions, business reports, and even help programmers write source code in different programming languages.

Despite the enormous potential of LLM, organizations need to be aware of the associated challenges and limitations. These include computational costs, the risk of bias in training data, the need for regular monitoring and tuning of models, and security and privacy challenges. It is also important to keep in mind that the results generated by models at the current stage of development require human oversight due to errors (hallucinations) that occur in them.

Source: DALL路E 3, prompt: Marta M. Kania (https://www.linkedin.com/in/martamatyldakania/)

What is LLMOps?

LLMOps, or Large Language Model Operations, is a set of practices for effectively deploying and managing large language models (LLMs) in production environments. With LLMOps, AI models can quickly and efficiently answer questions, provide summaries, and execute complex instructions, resulting in a better user experience and greater business value. LLMOps refers to a set of practices, procedures, and workflows that facilitate the development, deployment, and management of large language models throughout their lifecycle.

They can be seen as an extension of the MLOps (Machine Learning Operations) concept tailored to the specific requirements of LLMs. LLMOps platforms such as Vertex AI from Google (https://cloud.google.com/vertex-ai), Databricks Data Intelligence Platform (https://www.databricks.com/product/data-intelligence-platform) or IBM Watson Studio (https://www.ibm.com/products/watson-studio) enable more efficient management of model libraries, reducing operational costs and allowing less technical staff to perform LLM-related tasks.

Unlike traditional software operations, LLMOps have to deal with complex challenges, such as:

• processing huge amounts of data,
• training of computationally demanding models,
• implementing LLMs in the company,
• their monitoring and fine tuning,
• ensuring the security and privacy of sensitive information.

LLMOps take on particular importance in the current business landscape, in which companies are increasingly relying on advanced and rapidly evolving AI solutions. Standardizing and automating the processes associated with these models allows organizations to more efficiently implement innovations based on natural language processing.

Source: IBM Watson Studio (https://www.ibm.com/products/watson-studio)

MLOps vs. LLMOps 鈥� similarities and differences

While LLMOps evolved from the good practices of MLOps, they require a different approach due to the nature of large language models. Understanding these differences is key for companies that want to effectively implement LLMs.

Like MLOps, LLMOps relies on the collaboration of Data Scientists dealing with data, DevOps engineers and IT professionals. With LLMOps, however, more emphasis is placed on:

• performance evaluation metrics, such as BLEU (which measures the quality of translations) and ROUGE (which evaluates text summaries), instead of classic machine learning metrics,
• quality of prompt engineering 鈥� that is, developing the right queries and contexts to get the desired results from LLMs,
• continuous feedback from users 鈥� using evaluations to iteratively improve models,
• greater emphasis on quality testing by people during continuous deployment,
• maintenance of vector databases.

Despite these differences, MLOps and LLMOps share a common goal 鈥� to automate repetitive tasks and promote continuous integration and deployment to increase efficiency. It is therefore crucial to understand the unique challenges of LLMOps and adapt strategies to the specifics of large language models.

LLMOps key principles

Successful implementation of LLMOps requires adherence to several key principles. Their application will ensure that the potential of LLMs in an organization is effectively and safely realized. The following 11 principles of LLMOps apply to both creating, optimizing the operation and monitoring the performance of LLMs in the organization.

1. Managing computing resources. LLM processes such as training require a lot of computing power, so using specialized processors such as, Neural Network Processing Unit (NPU) or Tensor Processing Unit (TPU) can significantly speed up these operations and reduce costs. The use of resources should be monitored and optimized for maximum efficiency.
2. Constant monitoring and maintenance of models. Monitoring tools can detect declines in model performance in real time, enabling a quick response. Gathering feedback from users and experts enables iterative refinement of the model to ensure its long-term effectiveness.
3. Proper data management. Choosing software that allows for efficient storage and retrieval of large amounts of data throughout the lifecycle of LLMs is crucial. Automating the processes of data collection, cleaning and processing will ensure a constant supply of high-quality information for model training.
4. Data preparation. Regular transformation, aggregation and separation of data is essential to ensure quality. Data should be visible and shareable between teams to facilitate collaboration and increase efficiency.
5. Prompt engineering. Prompt engineering involves giving the LLM clear commands expressed in natural language. The accuracy and repeatability of the responses given by the language models, as well as the correct and consistent use of context, depend largely on the precision of the prompts.
6. Implementation. To optimize costs, pre-trained models need to be tailored to specific tasks and environments. Platforms such as NVIDIA TensorRT (https://developer.nvidia.com/tensorrt) and ONNX Runtime (https://onnxruntime.ai/) offer deep learning optimization tools to reduce the size of models and accelerate their performance.
7. Disaster recovery. Regular backups of models, data, and configurations ensure business continuity in the event of a system failure. Implementing redundancy mechanisms, such as data replication and load balancing, increases the reliability of the entire solution.
8. Ethical model development. Any biases in training data and model results that may distort results and lead to unfair or harmful decisions should be anticipated, detected, and corrected. Companies should implement processes to ensure responsible and ethical development of LLM systems.
9. Feedback from people. Reinforcing the model through user feedback (RLHF 鈥� Reinforcement Learning from Human Feedback) can significantly improve its performance, as LLM tasks are often open-ended. Human judgment allows the model to be tuned to preferred behaviors.
10. Chains and pipelines of LLMs. Tools like LangChain (https://python.langchain.com/) and LlamaIndex (https://www.llamaindex.ai/) allow you to chain multiple LLM calls and interact with external systems to accomplish complex tasks. This allows you to build comprehensive applications based on LLMs.
11. Model tuningOpen source libraries such as Hugging Face Transformers (https://huggingface.co/docs/transformers/index), PyTorch (https://pytorch.org/), or TensorFlow (https:/ /www.tensorflow.org/), help improve model performance by optimizing training algorithms and resource utilization. It is also crucial to reduce model latency to ensure application responsiveness.

Source: Tensor Flow (https://blog.tensorflow.org/2024/03/whats-new-in-tensorflow-216.html?hl=pl)

Summary

LLMOps enable companies to safely and reliably deploy advanced language models and define how organizations leverage natural language processing technologies. By automating processes, continuous monitoring and adapting to specific business needs, organizations can fully exploit the enormous potential of LLMs in content generation, task automation, data analysis, and many other areas.

While LLMOps evolved from MLOps best practices, they require different tools and strategies tailored to the challenges of managing large language models. Only with a thoughtful and consistent approach will companies be able to effectively use this breakthrough technology while ensuring security, scalability and regulatory compliance.

As LLMs become more advanced, the role of LLMOps is growing, giving organizations a solid foundation to deploy these powerful AI systems in a controlled and sustainable manner. Companies that invest in developing LLMOps competencies will have a strategic advantage in leveraging innovations based on natural language processing, allowing them to stay at the forefront of digital transformation.

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