Today’s companies must navigate a volatile economy, a competitive marketplace, and ever-changing consumer trends. For many, AI has been a lifeline.
AI’s tantalising spectrum of capabilities provides enterprises with the insights they need to inform critical business decisions, prompt financial returns, turbocharge products and, overall, remain competitive. As such, global AI adoption rates have more than doubled since 2017.
Unfortunately, AI, for many, is growing too complicated and expensive to utilise fully.
With pared-down workforces and less investment capital, 2023 is going to be the year of “do more with less.” Yet the demand for AI capabilities continues to outpace its affordability; as a result, developers will need to reinvigorate the processes and technology used to deploy and scale AI models.
Organisations must start to ask themselves: how can we continue to reap the rewards of AI-driven insights and products without hurting our bottom line?
The Growing Scale of AI Models
The first major friction point for current AI functionality is simply that AI models are the biggest they’ve ever been and therefore require huge compute resources.
Some of the most advanced models are now up to 10,000 times larger than they were four years ago, with trillions of parameters.
The larger AI models become, the more processing power they require. Plus, as inference continues to scale rapidly, AI cloud spend will rise dramatically. Organisations that do not have cost optimisation strategies are overspending up to 70% on cloud computing services.
To maintain AI’s momentum, developers will need to find ways to scale inference, thus effectively minimising compute costs. As AI inference continues to consume the majority of neural net compute power, it is likely that companies will pursue methods to reduce inference cloud costs through 2023. Otherwise, businesses will struggle to leverage AI cost-effectively to stay competitive.
In addition to improving their cloud operations, companies can scale inference by carefully accelerating AI models in a way that preserves their accuracy. If successfully executed, businesses will cut down on cloud and data centre processing time and save money, all while maintaining the effectiveness of their AI operations.
One way to mitigate this gap is by adopting optimisation solutions such as Neural Architecture Search (NAS)-based technologies – tools that use an automated algorithm to search through an aggregate space of millions of available model architectures to yield one uniquely suited to the inference environment that allows it to run more effectively.
Companies should also reevaluate the hardware they use to run their AI models, finding ways to utilise their investment in hardware better, particularly when their average hardware utilisation is already low If their hardware utilisation is high, other techniques may be necessary to improve its output, like generating faster throughput or improving the number of queries per second of inference workload.
To Win an Uphill Battle
In the years to come, we’re likely to see AI disrupting an array of new verticals. Developers have already been successful in adapting existing AI into entirely new products in various target industries. Such innovations are already taking hold with marketing content generation, where AI tools effectively produce text at scale, graphic design, where creative image generation is becoming seamless, and more.
However, the uphill battle of developing production-ready AI models for deployment is in danger of becoming too steep for developers. Oversized AI models and rising processing costs prevent many enterprises from capitalising on AI’s full potential.
Empowering developers to enhance AI models and better utilise the hardware that processes them are the key areas of innovation that will allow AI to scale effectively and sustainably throughout 2023.
If you liked reading this, you might like our other stories
Low-code Development Is Your Shortcut To Everyday AI
Artificial General Intelligence: The Future Of Decision AI