#65 Generative AI: Following the Footsteps of the Public Cloud's Convenience
A decade ago, I began my journey into the fascinating world of AI. Choosing to explore this field as a practitioner rather than a vendor, my focus remained steadfast on the applied aspects of AI. Over the years, I've witnessed the groundbreaking evolution of artificial intelligence, from the rise of deep learning and neural networks to the advent of generative AI. In recent times, the development of large language models (LLMs) has been particularly riveting. Today, it's intriguing to observe LLMs being discussed as casually in everyday conversations as car models or the latest films.
The Evolution and Operationalization of LLMs
The rapid mainstreaming of generative AI has primed the operationalization of large language models (LLMs) for critical conversation. Looking back at what might be termed the 'stone age' of AI, until late 2022, we referred to the operationalization of AI models as 'AIOps'. However, as we surge forward, I foresee the emergence of a new term to encapsulate this evolving space—'LLMOps'. As we continue on this trajectory, it wouldn't be surprising to see this term gain traction and become the standard.
Choosing an LLM: Lights, Camera, Action!
The process of choosing an LLM is much like deciding how to view a long-awaited movie; your preferences and requirements shape your decision, and the decision influences your experience. Let's explore this comparison through three scenarios. First, a film you've been eagerly awaiting is exclusively playing in theaters, akin to using OpenAI's APIs, which are renowned for their superior models. However, as with a public theater, they may not offer the same level of data security as other options.
The second scenario involves the movie being available on a streaming platform, comparable to using cloud providers. Much like the convenience of streaming a film from your home, cloud providers offer good models and robust security.
Lastly, consider the film being available solely on DVD. This scenario corresponds to running open-source models on-premises. While the quality might not be as superior as OpenAI or cloud providers, you have complete control over your environment, reminiscent of owning a DVD. However, much like setting up a DVD player, running open-source models on-premises can be complex and maintenance-heavy.
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The Attraction of Public Clouds: Prioritizing Convenience
In our digital age, public clouds have become a ubiquitous part of our landscape. Interestingly, the primary appeal of these clouds is not cost savings, but convenience. While there are instances when overhead costs may trigger discussions of repatriation - shifting workloads from the cloud back to on-premise - these discussions rarely translate into concrete action.
Cloud services offer a compelling attribute: the speed of infrastructure provisioning. The days of laborious 4-8 week set-ups are long gone. Today, cloud services have condensed this process into mere minutes, revolutionizing business operations and enabling rapid responses to market fluctuations.
While cost remains an important consideration, it often takes a backseat to the daily rigors of managing on-premise infrastructure. The convenience factor offered by cloud solutions tends to overshadow financial considerations, thereby underscoring the value of hassle-free, scalable, and flexible cloud services.
Conclusion
The intricate world of AI demands strategic implementation of large language models (LLMs). This process, which is far from simple, calls for a deep understanding of specific needs, preferences, and objectives. We can operationalize LLMs through OpenAI's models, public cloud, or on-premise implementations, each having its distinct advantages.
OpenAI's models serve the needs of early adopters and startups, while on-premise solutions offer superior control and data security. The public cloud, however, provides a balanced mix of quality, security, and convenience, making it an increasingly popular choice among enterprises. The future of AI promises to be fascinating, with the public cloud potentially leading the way in the operationalization of LLMs.