LLMOps – Generative AI needs new processes to deploy Large Language Models at the Edge

by Sanjay Mazumder

Venture Capitalists look at new ideas from one of two perspectives, either it has to be “better, faster, cheaper” or “brave new world”. Seldom do they pick the second one. 2023 is such a year. In the past few months, we’ve already seen how generative AI is setting the business agenda for major tech companies. What makes it different from previous “new worlds” concepts like Metaverse or Crypto Currency? First of all, it’s not an ethereal concept. It’s tangible and proven technology. And it’s not a tech trend only embraced by techies. Rather generative AI is already making its way into the hands of the public, in the form of chatbots, writing tools, and art-generation apps that are available to everyone. The potential of GenerativeAI is so vast that it’s destined to become a presence in every industry, weaving its way into every corner of content creation.

Enterprises are increasingly eager to incorporate large language models (LLMs) into their AI strategies due to their versatile applications. LLMs offer solutions to a broad spectrum of challenges, encompassing natural language processing (NLP), machine translation, text generation, question answering, summarization, chatbots, voice assistants, fraud detection, risk assessment, and customer service. What sets LLMs apart is their training on extensive datasets comprising text and code, enabling them to grasp intricate patterns and relationships. This inherent capability makes LLMs highly suitable for various tasks that involve the comprehension and processing of natural language.

However, enterprises are still evaluating the pros and cons of deploying LLMs in the cloud vs. the edge and making decisions on a case-by-case basis. The nature of the application plays a crucial role, as real-time response requirements make applications like chatbots and voice assistants more suitable for edge deployment, while applications such as fraud detection and risk assessment can tolerate longer response times and thus favor cloud deployment. Another consideration is the volume of data available, as LLMs rely on substantial datasets for training and operation. Enterprises with ample data resources may opt for edge deployment, whereas those with limited data may choose cloud deployment. Additionally, budget constraints are a significant factor, as deploying LLMs at the edge can be costlier than cloud deployment, making the latter more favorable for enterprises with limited financial resources. However, it is likely that we will see a trend towards deploying LLMs at the edge in the future. This is because edge deployment can help to address the latency, bandwidth, security, and cost issues that are associated with cloud deployment.

LLMOps, an emerging field focused on the deployment and management of large language models (LLMs) at the edge, presents a specialized subset of MLOps. While standard MLOps practices automate the development, deployment, and monitoring of machine learning models, LLMOps specifically address the unique challenges posed by LLMs. As LLMs gain widespread adoption, the importance of LLMOps is set to increase significantly.

LLMOps tackles bandwidth, latency, cost, and security challenges by providing tailored features for LLMs, including model compression, quantization, deployment, monitoring, and federated learning. Model compression tools offered by LLMOps facilitate the deployment and management of LLMs by compressing them without significantly compromising accuracy, thereby reducing the bandwidth requirements for sending the model to edge devices. Additionally, LLMOps provides tools for model quantization, a technique that minimizes the computational demands of LLMs by reducing the number of bits used to represent the model’s parameters, thereby optimizing their execution at the edge. The deployment tools offered by LLMOps ensure secure and reliable deployment of LLMs on edge devices. Furthermore, LLMOps equips enterprises with model monitoring tools, enabling the identification of performance issues and the implementation of necessary corrective measures. Lastly, LLMOps introduces federated learning, enabling the training of models on data stored on edge devices, thereby mitigating latency and bandwidth requirements during inference at the edge.

In upcoming blogs, we will delve into the promising value propositions that LLMOps offers in terms of performance and privacy when it comes to dealing with edge AI. These discussions will shed light on how LLMOps can further enhance the deployment and management of large language models (LLMs) at the edge, enabling organizations to achieve optimal performance while safeguarding sensitive data. Stay tuned to discover the compelling benefits and insights that LLMOps brings to the forefront of edge AI applications.

Originally posted on Linked-in: https://www.linkedin.com/pulse/llmops-generative-ai-needs-new-processes-deploy-large-mazumder/