Understanding LLMOps: Large Language Model Operations

In the rapidly evolving landscape of technology, Large Language Models (LLMs) have emerged as transformative game-changers, revolutionizing the way we interact with AI-powered applications. For this reason, tech enthusiasts and professionals need to explore the intricacies of LLMOps, spanning development, deployment, and maintenance. From understanding the rise of LLMOps to the selection of foundation models, adaptation to downstream tasks, and the nuances of evaluation, this blog equips readers to harness the potential of LLMs effectively. Through a step-by-step guide, we will shed light on the complexities of LLMOps, offering valuable insights and practical knowledge to navigate the dynamic landscape of Large Language Models in the contemporary AI ecosystem.

Large Language Models Definition: What Is a Large Language Model?

LLMOps, an abbreviation for Large Language Model Operations, encapsulates the essence of MLOps tailored specifically for Large Language Models (LLMs). In essence, LLMOps represents a novel toolkit and a set of best practices designed to proficiently navigate the entire lifecycle of applications powered by Large Language Models. This comprehensive approach spans the developmental phase, deployment procedures, and ongoing maintenance.

To grasp the concept of LLMOps as “MLOps for LLMs,” it is crucial to elucidate the terms LLMs and MLOps:

LLMs, denoting Large Language Models, are sophisticated deep learning models adept at generating human language outputs. These models boast billions of parameters and undergo training on vast corpora comprising billions of words, hence earning the designation of large language models.

MLOps, short for Machine Learning Operations, encompasses a suite of tools and optimal methodologies tailored to oversee the lifecycle of applications propelled by machine learning.

Why the Rise of LLMOps?

The emergence of Early Large Language Models (LLMs) like BERT and GPT-2 dates back to 2019, yet it is only now, nearly five years later, that the concept of LLMOps is undergoing a meteoric rise. This surge can be primarily attributed to the heightened visibility of LLMs following the unveiling of ChatGPT in December 2022.

In the aftermath, a plethora of applications have harnessed the potency of LLMs, ranging from renowned chatbots like ChatGPT to more personalized ones such as Michelle Huang engaging in conversations with her childhood self. Additionally, LLMs have been instrumental as writing assistants for tasks like editing or summarization (e.g., Notion AI), and have found specialized applications in domains like copywriting (e.g., Jasper and copy.ai) and contracting (e.g., lexion).

The spectrum expands further to encompass programming assistants, aiding in tasks from code composition and debugging (e.g., GitHub Copilot) to code testing (e.g., Codium AI) and even identifying security threats (e.g., Socket AI).

As individuals delve into the development and deployment of LLM-powered applications, the shared experiences highlight a notable sentiment, encapsulated by Chip Huyen’s insight: “It’s easy to make something cool with LLMs, but very hard to make something production-ready with them.”

The realization has clarified that constructing production-ready LLM-powered applications introduces distinct challenges, setting it apart from the conventional approach of building AI products with classical ML models. In response to these challenges, there is a growing imperative to forge new tools and best practices specifically tailored to navigate the nuanced lifecycle of LLM applications, thus giving rise to the prevalent adoption of the term “LLMOps.”

How Do Large Language Models Work?

The procedures encompassed in LLMOps share certain parallels with those of MLOps. However, the process of constructing an application powered by Large Language Models (LLMs) deviates significantly due to the advent of foundation models. Rather than embarking on the arduous journey of training LLMs from scratch, the focal point shifts towards the adaptation of pre-trained LLMs for downstream tasks.

A pivotal trend is reshaping the landscape of training neural networks— the conventional paradigm of training a neural network from scratch on a specific target task is swiftly becoming antiquated. This shift is particularly pronounced with the rise of foundation models, exemplified by GPT. These foundational models, crafted by a select few institutions equipped with substantial computing resources, usher in a paradigm where achieving proficiency in various applications is attained through nimble fine-tuning of specific sections of the network. This approach is complemented by strategies such as prompt engineering or an elective process of distilling data or models into more streamlined, purpose-specific inference networks.

Step 1: Selection of a Foundation Model

Foundation models represent Large Language Models (LLMs) that undergo pre-training on extensive datasets, rendering them versatile for a myriad of downstream tasks. The process of training a foundation model from scratch is inherently intricate, time-intensive, and financially burdensome, necessitating substantial resources that only a select few institutions possess.

To underscore the magnitude of this undertaking, consider a study conducted by Lambda Labs in 2020, revealing that the training of OpenAI’s GPT-3, boasting a colossal 175 billion parameters, would demand a staggering 355 years and incur costs amounting to $4.6 million when utilizing a Tesla V100 cloud instance.

In the contemporary AI landscape, a pivotal epoch is unfolding, often likened to the “Linux moment” within the community. Developers find themselves confronted with a choice between two categories of foundation models, each entailing a delicate balance between performance, cost, ease of use, and flexibility: the proprietary models and their open-source counterparts.

Proprietary models stand as exclusive, closed-source foundation models, typically owned by companies endowed with substantial expert teams and sizable AI budgets. Distinguished by their expansive scale, these models often outperform their open-source counterparts and boast user-friendly, off-the-shelf accessibility.

However, the primary drawback associated with proprietary models lies in their costly Application Programming Interfaces (APIs). Moreover, these closed-source foundation models offer limited or no flexibility for adaptation by developers, presenting a potential constraint in customization.

Notable providers of proprietary models include industry leaders such as OpenAI, with offerings like GPT-3 and GPT-4, co:here, AI21 Labs featuring Jurassic-2, and Anthropic showcasing Claude.

In contrast, open-source models find a communal hub on platforms like HuggingFace. While these models tend to be more modest in size and capabilities compared to their proprietary counterparts, they offer a distinct advantage in terms of cost-effectiveness and greater adaptability for developers.

Prominent examples of open-source models include Stable Diffusion by Stability AI, BLOOM by BigScience, LLaMA or OPT by Meta AI, and Flan-T5 by Google. Additionally, projects like GPT-J, GPT-Neo, or Pythia spearheaded by Eleuther AI contribute to the expanding landscape of accessible, community-driven AI models.

Step 2: Adaptation to Downstream Tasks

Upon selecting your foundation model, accessing the Large Language Model (LLM) becomes achievable through its Application Programming Interface (API). If you are accustomed to interfacing with other APIs, navigating LLM APIs might initially evoke a sense of unfamiliarity, as the correlation between input and output may not always be apparent in advance. When presented with a text prompt, the API endeavors to generate a text completion that aligns with the provided pattern.

To illustrate, consider the usage of the OpenAI API. Input is furnished to the API in the form of a prompt, such as: “Correct this to standard English:\n\nShe no went to the market.”

The API response will furnish the completion result as follows: `[‘choices’][0][‘text’] = “She did not go to the market.”

Despite the formidable power of Large Language Models (LLMs), they are not omnipotent. This raises a pivotal question: How can one guide an LLM to produce the desired output? Addressing concerns voiced in the LLM in production survey, issues such as model accuracy and hallucinations emerge. Achieving the desired output from the LLM API may necessitate iterative adjustments, and instances of hallucinations can occur when the model lacks specific knowledge.

To navigate these challenges, adaptation of foundation models for downstream tasks becomes imperative. One approach is Prompt Engineering, a technique that involves refining the input to align the output with predefined expectations. Various strategies, as detailed in the OpenAI Cookbook, can enhance the efficacy of prompts. Providing examples of the expected output format resembles a zero-shot or few-shot learning setting. Tools like LangChain and HoneyHive have already surfaced, offering support in managing and versioning prompt templates.

Fine-tuning pre-trained models, a well-established technique in machine learning, stands as a valuable approach to enhance the performance of your model on a particular task. While this endeavor intensifies the training efforts, it concurrently mitigates the cost of inference. Notably, the expense associated with Large Language Model (LLM) APIs hinges on the length of input and output sequences. Consequently, curtailing the number of input tokens not only optimizes model efficiency but also results in diminished API costs, as the necessity to furnish examples within the prompt is alleviated.

External data poses a crucial dimension for augmenting foundation models, given their inherent limitations such as a lack of contextual information and susceptibility to rapid obsolescence (e.g., GPT-4 trained on data predating September 2021). The potential for hallucination in Large Language Models (LLMs) underscores the necessity of providing access to pertinent external data. Existing tools like LlamaIndex (GPT Index), LangChain, or DUST serve as pivotal interfaces, facilitating the connection or “chaining” of LLMs with external agents and data sources.

An alternative strategy involves the extraction of information in the form of embeddings from LLM APIs (e.g., movie summaries or product descriptions). Applications can then be constructed atop these embeddings, enabling functionalities such as search, comparison, or recommendations. In cases where the np.array proves insufficient for embedding storage in long-term memory, vector databases like Pinecone, Weaviate, or Milvus offer robust solutions.

Given the rapid evolution of this field, a spectrum of approaches emerges for harnessing LLMs in AI products. Examples include instruction tuning/prompt tuning and model distillation, indicative of the diverse pathways in leveraging the potential of LLMs.

Step 3: Evaluation

Within classical MLOps, the validation of machine learning models typically involves assessing their performance on a hold-out validation set, leveraging metrics to gauge efficacy. However, the evaluation of Large Language Models (LLMs) introduces a distinctive challenge—how does one discern the quality of a response? Determining the merit of a response, whether it is deemed satisfactory or lacking, becomes a nuanced endeavor in the context of LLMs. Presently, organizations are navigating this complexity through the adoption of A/B testing methodologies.

Step 4: Deployment and Monitoring

The completions generated by Large Language Models (LLMs) exhibit significant variations across different releases. For instance, OpenAI regularly updates its models to address concerns such as inappropriate content generation, including hate speech. A tangible outcome of this evolution is evident in the proliferation of bots when searching for the phrase “as an AI language model” on platforms like Twitter.

This underscores the imperative for vigilant monitoring of the evolving landscape of underlying API models when developing applications powered by LLMs. Recognizing the dynamic nature of LLM behavior necessitates a proactive approach in adapting to changes and addressing emerging challenges.

Acknowledging this need, a suite of tools has already emerged to facilitate the monitoring of LLMs, exemplified by platforms like Whylabs and HumanLoop. These tools play a pivotal role in enabling developers and organizations to stay attuned to shifts in LLM behavior and make informed decisions regarding the deployment and management of LLM-powered applications.

How to Build a Large Language Model?

The pivotal stages encompass choosing a platform, opting for a language modeling algorithm, conducting training sessions for the language model, executing the deployment of the language model, and ensuring the ongoing maintenance of the language model.

A robust, varied, and substantial training dataset is paramount for crafting tailored Large Language Models (LLMs), with a recommended size of at least 1TB. The design process for LLM models can be carried out either on-premises or by leveraging the cloud-based offerings of Hyperscalers. Cloud services provide a straightforward, scalable solution, offloading technology burdens through the utilization of well-defined services. Employing cost-effective strategies involves leveraging open-source and free language models, contributing to overall expense reduction while ensuring efficiency.

Option 1: Utilizing On-Prem Data Centers for LLMs

Leverage your on-premises data center hardware to create Large Language Models (LLMs), acknowledging the costliness of hardware components such as GPUs. Explore free Open-Source models like HuggingFace BLOOM, Meta LLaMA, and Google Flan-T5. Emerging models like HuggingFace and Replicate can serve as API hosts. Enterprises may opt for established LLM services like OpenAI’s ChatGPT or Google’s Bard.

Pros

• Full control over data processing, enhancing privacy.
• Customizable models tailored to specific use cases.
• Potential cost efficiency over time.
• Competitive edge with a unique, customized “secret sauce.”

Cons

• Requires technical expertise and infrastructure.
• In-house model upgrades, potentially costly.
• Dependency on in-house ML professionals.
• Onboarding new hires may slow progress.

Option 2: On-Prem Hardware for Custom LLM Creation

Create bespoke LLMs using on-prem hardware:

• Utilize platforms like Anaconda for LLM building resources.
• Leverage Python to build LLM libraries and dependencies.
• Train models with TensorFlow or Hugging Face pre-trained models like GPT-2.
• Fine-tune and customize using Python based on specific goals.

Option 3: Utilizing Hyperscalers

Explore services from AWS Sagemaker, Google GKE/TensorFlow, and Azure Machine Learning for LLM creation in the public cloud. AWS Machine Learning services, Google Cloud AI Platform, and Azure Machine Learning offer streamlined processes for data processing, model training, deployment, and monitoring.

Option 4: Subscription Model

Opt for API subscriptions from providers like OpenAI, Cohere, and Anthropic.

Pros

• No infrastructure setup required, simplifying access.
• Uniform API access for integration.
• Flexibility to switch providers.
• Time and cost savings without ML Ops setup.

Cons

• Data sent to third parties may pose privacy concerns.
• Adoption challenges for enterprise customers.
• Subscription prices determined by service level agreements and pricing strategies.
• Scaled closed-source solutions may incur higher costs compared to in-house models.

Closing Thoughts

In the exploration of Large Language Models (LLMs) and LLMOps, a fusion of MLOps principles with the unique challenges of LLMs emerges. LLMOps, a toolkit for LLM applications, spans development, deployment, and maintenance. The surge in LLMOps parallels the growth of LLM visibility, exemplified by milestones like ChatGPT. Addressing challenges in making LLM applications production-ready, the paradigm shift involves choosing foundation models, fine-tuning, and adapting to downstream tasks. A dichotomy between proprietary and open-source models unfolds, while evaluation in LLMOps demands innovative methods like A/B testing. The dynamic nature of LLMs necessitates vigilant deployment and monitoring, facilitated by emerging tools like Whylabs and HumanLoop. This journey signifies a convergence of technology and operational best practices, shaping the transformative potential of Large Language Models in AI applications.