This is the second part of the 2-parts post related to LLM Infrastructure. Please check the first one before proceeding.
Things to Consider
In the eternal discussion of build vs buy, it’s important to include the costs of running and maintaining your infrastructure once you’ve built it. Two or three engineers working over a couple of quarters to build it might not sound too bad at first, but the larger cost happens after you’ve built it. Whoever inherits LLMOps will have to consider the following:
- The complexity of Models – LLM are highly complex and require a deep understanding of machine learning (ML) and natural language processing (NLP); if your existing engineering team doesn’t have this expertise, you will have to develop or hire that expertise
- Model Size – generative models are massive, leading to storage and memory challenges; managing and deploying these models efficiently can be daunting
- Scalability – scaling genAI solutions to handle increased usage and traffic requires careful planning and resource management
- Data Requirements – training generative models often require large datasets, which may be difficult to acquire or curate; data collection, preprocessing, and cleaning can be time-consuming and resource-intensive
- Computational Resources – running generative models, especially large ones, demands significant computational power; access to and affordability of such resources can be a barrier
- Cost – the cost of running generative AI models can be significant, including not only the model licenses and computational expenses but also costs related to data collection, storage, and maintenance
- Rapidly Changing Landscape – the field of generative AI is rapidly evolving, with new models and techniques emerging regularly; staying up-to-date can be demanding
- Accessibility and Collaboration – while your engineering resources may be able to work with genAI LLM directly, your non-technical teams like marketing, product, and business development, will have a steeper learning curve, limiting the potential for collaboration across your organization
- Content Validation and Evaluation – measuring the quality and trustworthiness of generated content is challenging; developing robust evaluation methods is crucial; your teams, both technical and non-technical, will need a way to evaluate your content
Running LLMOps infrastructure requires a multidisciplinary approach that combines expertise in AI research, infrastructure management, ethics, security, and compliance. Addressing these challenges is essential to deliver reliable, efficient, and responsible AI-powered services. Despite these challenges, generative AI offers tremendous potential for creativity, problem-solving, and innovation.
With Engenai, you can dramatically accelerate your time to market while spending just a fraction of what it would take to build and run your own LLMOps infrastructure. Your Engenai instance can be configured, managed, and accessed with low or no code. Get your teams up and running with genAI in a couple of weeks instead of months.
