I was first exposed to a neural network circa 1988, when a labmate was trying to characterize the cutting process on a milling machine, predict when it was going to fail, and provide guidance to the millwright. I recall that even training and running a basic neural network successfully was a challenge.
Today, the picture is starkly different. Sophisticated neural networks identify hard-to-detect issues at lightning speed. The ML process is, comparatively, smooth and AI is in actual real world use. It is being productized, implemented, and deployed in ways as diverse as the many different markets and business problems that exist. This near future view is possible because of the state AI is in today – one of maturity where companies are no longer asking how it works, but what problems it can solve. This change in evaluation by enterprises does not only represent a deeper fundamental understanding of AI technology but a recognition that the technology, without a doubt, provides value.
The next wave of AI is moving out of more laboratories and into operations. What form and direction AI takes will be debated for years to come as people continue to find new and interesting challenges for it to solve. AI solutions are no longer entering the backdoor of an enterprise with a company’s innovation team. Instead, they are being ushered in through the front by the likes of operation teams working to find practical, day-to-day solutions for their problems. Being brought to the shop floor presents new challenges for AI vendors to be ready to solve, like the issues of privacy, infrastructure and training, which are questions considered right alongside the fundamental cost-benefit question.
The last two years gave AI adoption the impetus it needed to become even more essential in the future–-instability in systems once thought reliable before the pandemic and Ukraine crisis, has forced companies to adopt new tools to bolster adaptability (mobile, cloud, etc.) There has been a breakneck speed in AI innovation as companies seek ways to empower employees with better decision-making tools and drive innovation from within their own companies.
Consider, for example, MLOps, the newly emerging area that offers the tooling that companies use to harmoniously orchestrate all of the complex componentry of an AI system (data prep, model training, model deployment, model monitoring and more) with the operational rigor of a battleship.
As companies double down on AI, MLOps is increasingly becoming operations critical. Engineers with an MLOps background will become highly sought after and will likely remain so well into the foreseeable future. Like its sister function, DevOps, which was created to support the newly emerging cloud infrastructures, MLOps helps AI teams meet the imperative of maintaining all the MLOps components to properly iterate and continuously improve during the artificial intelligence lifecycle.
With this growing need comes opportunity! With AI becoming embedded at the core of everything, from architecture to operations, AI teams will need employees with the right set of skills to create and execute their engineering capabilities. Indeed, when AI is ubiquitous, MLOps will eventually become a regular part of the organization’s operations. And, MLOps engineers will be in high demand.
AI is going global. The technology will become key to the success and development of businesses in emerging markets. Inquiries we receive from BRIC nations and other emerging countries show that AI is no longer for developed countries only. Just as these countries leapfrogged their way to the front of the mobile world by going from no phones/landlines to cell phones everywhere, AI solutions allow emerging countries to quickly overcome existing infrastructure gaps to better compete globally. In fact, I might go as far as to say that, being unfettered, they can deploy AI-based enterprise systems more correctly and gain greater value. On the cost side, AI can help increase productivity without the need to build expensive and time-consuming infrastructure. On the usage side, the experiences can be designed to be AI-first—meaning that the probabilistic nature of AI can be made human-consumable using first principles. By lowering barriers like cost-to-entry, emerging countries are seizing the opportunity that AI solutions represent. They’re even more ready to dive in and commit to bringing themselves into the now and future than perhaps even their developed counterparts.
The market for AI solutions is only going to get bigger. Demand for these solutions will extend into critical business operations areas. AI can propel a company’s scaling into new verticals and markets like nothing before has been able to do. While human creativity is the best and most sophisticated tool that exists, AI can empower people’s creativity to reach heights that were simply unreachable before. Enterprises that are optimizing and adopting AI solutions now, will be better positioned to create insights, drive collaboration, elevate experimentation, and exploit opportunities that never existed before adopting AI. Now is the time to get on board. Or be left behind.
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