Are you looking to implement AI in your product or service but need help figuring out where to start? For example, are you deciding whether to fine-tune, prompt engineer, or train a custom model?
It can be overwhelming with all the options and advancements in AI technology. However, maximizing flexibility and agility must be the focus of your AI product strategy if you want to succeed.
What does it mean for your AI product to be flexible and agile? Quickly improving by integrating with better and cheaper models as companies like OpenAI release them.
Personal Experience with Flexible AI Systems
It’s easy to see the importance of flexibility and agility in AI product strategy from recent developments in the market space. For example, many of my past projects have included OpenAI tools like GPT-3 to enhance functionality using natural language generation.
At times, given the perceived value of proprietary data, we could have, and with good reason, decided to invest in training custom models. However, I’m glad we didn’t.
Within a year, companies like OpenAI released newer, better, and cheaper variations of GPT-3. But, because we had a flexible and agile approach, we could quickly try out the latest updates, test how they could improve the product, and quickly move to provide better customer results by incorporating the latest advancements.
A Hypothetical Story of a Fragile AI System
What if the above story didn’t happen that way? Imagine if we had instead decided to train custom models. Early on, it seemed plausible that we could develop a better solution than what was currently available through OpenAI. Moreover, the cost of talent and computing needed for training a custom model might have seemed acceptable for us to build what we perceived as a competitive advantage.
It was 2021, so we would have benchmarked our success against the earlier models released by OpenAI. At first, we might have even managed to produce a comparable custom model.
Then 2022 would’ve rolled in. InstructGPT got released in January, and our custom model would have suddenly lost ground on our benchmarks. So we would have to invest more in updating our custom model, and a few months later, it might have seemed like a good idea when we started to catch up.
But, before we could celebrate, the text-davnici-002 model by OpenAI would have been released. So this time, we would’ve felt farther behind than we did with the release of InstructGPT.Â
By this point, we would have been deeply invested monetarily in Data Science talent, GPUs, and other decisions necessary to maintain the pride of “doing it” without relying on those cloud AI services. We would also have been fully committed to our path, including telling ourselves stories about why our competitors that rely on cloud providers, like OpenAI, are inferior.
Within the same year, text-davnici-003 and ChatGPT get released. In addition, rumors are that GPT-4 is just around the corner. And, after significant investment, our disillusionment that training our custom models is a sufficient differentiator starts to fade.
Comparison to Cloud Computing
Some of us remember when companies started transitioning to cloud servers for web hosting. With companies like OpenAI, Hugging Face, and Anthropic, AI computing is already reaching that inflection point.
Soon after the introduction of cloud computing, some professionals were still advising against using the cloud for various reasons. However, unfortunately, they likely prevented themselves and their clients from capitalizing on the coming wave of cloud solutions that supported an entire generation of new technology businesses.
Instead of leveraging off-the-shelf AI, like the tools from OpenAI, what if you decide to compete with the cloud providers head-on by training custom models? The answer is the same as if you choose to host your servers instead of connecting to backbone architecture like that provided by AWS, Azure, and GCP.
Why Flexibility and Agility Matter
AI technology is rapidly evolving and improving, and what’s state-of-the-art today may be outdated tomorrow. A flexible and agile AI strategy can maximize your ability to interchange off-the-shelf AI services as new, better, and cheaper options are released.Â
Maximizing flexibility and agility in your AI system will save you time and resources in the long run while ensuring you always use the best AI technology available to deliver the best results to your customers.
Practical Steps to Achieve Flexibility and Agility
Start with a modular approach.
Designing your AI product or service should begin with modular, easily updated components. This means maximizing the utilization of pre-trained models instead of relying on training custom models. These components should make it easy to test and integrate new AI technologies as they become available.
Regularly test new alternative components.
It’s easy to focus on what you’re doing right instead of what can be improved. However, it’s essential to experiment with similar models from different providers. This practice also ensures you’re flexible enough to ensure that you can change to better models in the future.
Stay up-to-date on necessary developments.
Staying informed on the latest AI technologies is challenging and nearly impossible to maintain for the field of AI as a whole. So first, it’s necessary to have a deep understanding of the components that make up your particular AI system. Then you can focus on the advancements that impact your system directly.
Conclusion
Implementing a flexible and agile AI product strategy is critical in today’s dynamic AI environment. Utilizing a modular approach, maintaining an open perspective, and staying informed will enable your AI product or service to provide the best customer outcomes.
Do you need help designing a flexible AI system for your business? Schedule a consultation.