Why manage transfer learning of large language models and what is included in this management: getting acquainted with the MLOps extension for LLM called LLMOps.
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How did LLMOps come to be?
Large language models, embodied in generative neural networks (ChatGPT and other analogues), have become the main technology of the outgoing year, which is already actively used in practice by both individuals and large companies. However, the process of training LLM (Large Language Model) and their implementation in industrial use must be managed in the same way as any other ML system. A good practice for this has become the MLOps concept, aimed at eliminating organizational and technological gaps between all participants in the development, deployment and operation of machine learning systems.
As the popularity of GPT networks and their implementation in various application solutions grows, there is a need to adapt the principles and technologies of MLOps to transfer learning used in generative models. This is because language models are becoming increasingly large and complex to maintain and manage manually, which increases costs and reduces productivity. To avoid this, LLMOps, a type of MLOps that oversees the LLM lifecycle from training to maintenance using innovative tools and methodologies, can help.
LLMOps focuses on the operational capabilities and infrastructure required to fine-tune existing base models and deploy these improved models as part of a product. Because base language models are huge, such as GPT-3, which has 175 billion parameters, they require a huge amount of data to train, as well as time to map the computations. For example, it would take over 350 years to train GPT-3 on a single NVIDIA Tesla V100 GPU. Therefore, an infrastructure that can run GPU machines in parallel and process huge data sets is essential. LLM inference is also much more resource-intensive than more traditional machine learning, as it is not a single model, but a chain of models.
LLMOps provides developers with the necessary tools and best practices for managing the LLM development lifecycle. While the ideas behind LLMOps are largely the same as MLOps, large base language models require new methods, guidelines, and tools. For example, Apache Spark in Databricks works great for traditional machine learning, but it is not suitable for fine-tuning LLMs.
LLMOps focuses specifically on fine-tuning base models, since modern LLMs are rarely trained entirely from scratch. Modern LLMs are typically consumed as a service, where a provider such as OpenAI, Google AI, etc. offers an API of the LLM hosted on their infrastructure as a service. However, there is also a custom LLM stack, a broad category of tools for fine-tuning and deploying custom solutions built on top of open-source GPT models. The fine-tuning process starts with an already trained base model, which then needs to be trained on a more specific and smaller dataset to create a custom model. Once this custom model is deployed, queries are sent and the corresponding completion information is returned. Monitoring and retraining a model is essential to ensure its consistent performance, especially for LLM-driven AI systems.
Rapid engineering tools allow contextual training to be performed faster and cheaper than fine-tuning, without requiring sensitive data. In this case, vector databases extract contextually relevant information for specific queries, and prompt queries can optimize and improve model output based on patterns and chaining.
Similarities and differences with MLOps
In summary, LLMOps facilitates the practical application of LLM by incorporating operational management, LLM chaining, monitoring, and observation techniques that are not typically found in conventional MLOps. In particular, prompts are the primary means by which humans interact with LLMs. However, formulating a precise query is not a one-time process, but is typically performed iteratively, over several attempts, to achieve a satisfactory result. LLMOps tools offer features to track and version prompts and their results. This facilitates the evaluation of the overall performance of the model, including operational work with multiple LLMs.
LLM chaining links multiple LLM invocations in a sequential manner to provide a single application function. In this workflow, the output of one LLM invocation serves as the input to another to produce the final result. This design approach represents an innovative approach to developing AI applications by breaking down complex tasks into smaller steps. Chaining removes the inherent limitation on the maximum number of tokens that LLM can process simultaneously. LLMOps simplifies chaining management and combines it with other document retrieval methods, such as vector database access.
LLMOps’s LLM monitoring system collects real-time data points after a model is deployed to detect degradation in its performance. Continuous, real-time monitoring allows you to quickly identify, troubleshoot, and resolve performance issues before they affect end users. Specifically, prompts, tokens and their length, processing time, inference latency, and user metadata are monitored. This allows you to notice overfitting or changing the underlying model before performance actually degrades.
Monitoring models for drift and bias is also critical. While drift is a common problem in traditional machine learning models, as we’ve written about here, monitoring LLM solutions with LLMOps is even more important due to their reliance on underlying models. Bias can arise from the original datasets on which the base model was trained, custom datasets used for fine-tuning, or even from human evaluators judging fast completion. A thorough evaluation and monitoring system is needed to effectively remove bias.
LLM is difficult to evaluate using traditional machine learning metrics because there is often no single “right” answer, whereas traditional MLOps relies on human feedback, incorporating it into testing, monitoring, and collecting data for use in future fine-tuning.
Finally, there are differences in the way LLMOps and MLOps approach application design and development. LLMOps is designed to be fast, whereas traditional MLOps projects are typically iterative, starting with existing proprietary or open-source models and ending with custom fine-tuned or fully trained models on curated data.
Despite these differences, LLMOps is still a subset of MLOps. That’s why the authors of The Big Book of MLOps from Databricks have included the term in the second edition of this collection, which provides guiding principles, design considerations, and reference architectures for MLOps.
