Why Do Large Language Models (LLMs) Need a Robust Delivery Mechanism?
The artificial intelligence (AI) industry has been working for years on improving natural language processing and understanding. The latest intelligent technology in this regard is the Large Language Model (LLM). These models have transformed the way machines process natural language text at exponentially larger scales.
Large Language Models
Large Language Models (LLMs) are artificial intelligence technology that can interpret, translate and summarize natural language texts using advanced machine learning algorithms. These are complex models trained on enormous amounts of data enabling them to generate human-like sentences.
From fast coding and better content creation to advanced search capabilities and personalization, large language models find their usage in various business applications like content generation, search engines, customer service, and more. Large language models are a powerful innovation for automating and improving processing tasks involving natural language text.
The size of large language models generally spans tens of gigabytes with trillions of parameters. A parameter refers to the values an LLM can independently update as the model learns. These values are learned from existing training data and characterize the skill of a large language model for solving a specific problem, e.g., text generation.
How LLMs Affect The Model Production Process
An efficient large language model almost definitely means the commencement of a positive influx in model creation. Whether you choose to fine-tune the same model or create a new model using the data from an existing LLM, there is significant potential for creating faster and more structured language models.
While creation of models will influx due to LLM, the operational aspect of delivering these models into production is yet to be solved by most organizations. Research shows that only 87% of models make it to production. Supporting the operational infrastructured to build, train and deploy models is crucial in order to properly leveraghe the power of LLM.
This article will discuss the challenges of productionizing large language models and how Qwak's robust delivery system resolves these challenges:
The Current State of Large Language Models: Going Beyond Language
Since the advent of AI in the 1950’s, computer scientists have been attempting to enable machines to process and converse in natural language like humans. As data became more complex, unsupervised learning emerged to enable models to train on vast amounts of unstructured data. This advancement paved the way for the first large language model in 2017; the Transformer model. Google's BERT and other transformer models eventually evolved to give rise to the development of large language models with millions of parameters, each more sophisticated and advanced than its predecessor. In 2020, OpenAI released its GPT-3, the first large language model to cross a hundred billion parameters.
In November 2022, after several improvements in GPT-3, OpenAI released its latest large language model prototype, ChatGPT.
Challenges associated with ChatGPT
ChatGPT is an artificial intelligence chatbot specializing in dialogue, developed by fine-tuning OpenAI's GPT-3 using reinforcement learning and supervised learning. It has been optimized for conversational AI to generate versatile and natural-sounding text responses so people can get a better understanding of language and be able to communicate better.
Although the demo of ChatGPT has become a viral sensation, deploying the ChatGPT model at production scale will be a huge operational challenge requiring complex infrastructural customizations. The only way to tackle these operation and infrastructure complexities is by implementing proper machine learning workflows to transform ChatGPT into a reliable LLM product.
End-to-End Automated LLM Development and Deployment Pipeline
Despite the numerous applications of large language models, most industries need more intensive computing and engineering resources to utilize them. As LLMs grow in size and complexity, model training and deployment also get significantly more challenging.
It is extremely difficult to structure an architecture that is able to keep up with the intensive scalability requirements of large language models.
Unfortunately, the traditional software development workflows of building, testing, and deploying models, do not apply to large language models. For LLMs, all the workflow stages demand specific hardware and software components. Building these components and training and testing on them is a complex and time-consuming task.
In addition to setting up a complex infrastructure, the monitoring and maintenance of large language models remains a growing challenge. Before you can get to the root of a quality issue, your LLM will likely have drifted or degraded.
To address these challenges, large language models need robust machine learning pipelines to train, develop, and deploy at scale automatically. Machine learning model operations (MLOPs) is the only approach that can prepare you for the predictable and unpredictable complexities associated with LLMs.
Implementing MLOPs in your LLM productionization requires your organization to reshape its deployment strategies. However, even with practical strategies, your organization will likely need more resources for large-scale operationalization.
To lead your large language models to production, you need specialized tools, like Qwak’s Build tool, that offer end-to-end LLM development and deployment pipelines for automatically handling the evolving nature of your large language models.
4 Key Challenges in Moving LLMs to Enterprise-Scale Production
Here are the four most common challenges of moving your large language models to enterprise-scale production:
Cost-intensive architecture, processing, and experimentation
It should be no surprise that models of such a massive scale are highly costly to operationalize. The computation of large language models requires extensive architecture to distribute the parameters across multiple processing engines and memory blocks. On top of that, the experimentation costs will keep adding up to the point where you will exhaust all your resources before your model makes it to production.
Issues associated with language misuse
Large language models are built using massive amounts of data from disparate sources. The problem with collecting vast heterogeneous data is the biases that stem from the data source's culture and society. Moreover, verifying the credibility of so much information takes time and effort. When a large language model is trained on biased and potentially false data, the model amplifies the data's discrepancies, leading to erroneous and discriminatory outcomes.
In addition to language source risks, making LLMs understand human logic and the different contexts behind the same data is very challenging. The most critical challenge is to perfectly reflect the diversity of human beliefs and opinions in large language models.
Fine-tuning for downstream tasks
Large language models are generally accurate and efficient for large-scale data. However, it can be challenging to repurpose these models for specific domains and tasks. This repurposing requires fine-tuning existing large language models to create smaller models for specific downstream tasks.
Although fine-tuned large language models offer the same performance benefits as their parent model, it can take time to get them right. Details like knowing what data to use, choosing the hyperparameters, and choosing base models to tune are crucial to these models and equally hard to figure out. You need to accurately work out these details to maintain the explainability of your fine-tuned model.
Even if your enterprise has the budget for large-scale distributions, finding a suitable mode of hardware configuration and distribution is another challenge that awaits. As there is no one-size-fits-all hardware stack for LLMs, it is up to you to create an optimal hardware plan for your model. Moreover, you will also require optimized algorithms to help your computational resources adjust to your scalable LLMs.
Since parallel and distributed computing resources and expertise are rare, there will be an additional burden on your organization to find computing experts for large language models.
Leverage Qwak for Production-Worthy Large Language Models
Qwak is a fully-managed machine learning platform that unifies machine learning engineering and data operations. It is designed to abstract away all the complexities that come with model deployment, integration, and optimization. It provides an agile infrastructure for enterprises to continuously productionize their large language models at scale.
If you are ready to unlock the true potential of your LLM applications and create a competitive advantage for your organization, check out Qwak and start today for free.