AI is more than just a buzzword—it’s a driving force behind major technological advancements. For businesses, early AI adoption is critical, but proper execution today means success tomorrow. Cloud deployments ultimately introduce issues as demand increases—latency stifles real-time decision-making and data throughput plus computational load drive rapidly increasing costs. The solution? Run your AI model where your devices are—on the edge.
For all of the benefits of running AI on the edge, however, operationalizing can present challenges. The most significant issue comes with AI model deployment. Allow me to explain.Content delivery of all types—files, applications and system updates, for example—is a struggle for many organizations, and AI model deployments only exacerbate this issue. There are several reasons for this.
To overcome these challenges, organizations need a comprehensive strategy that encompasses the entire AI life cycle, starting with the devices.Just as AI will continue to impact the way devices are used, your strategy around AI model, app and content distribution also has to evolve. Fortunately, a solution already exists in the world of software development: DevOps.
You might be asking yourself what DevOps has to do with device management. DevOps practices are about alignment between development and ops teams, and extending that concept beyond software development to the edge is where the magic happens. With a DevOps philosophy applied to device management, your development and IT teams can work together to build, test, apply and iterate AI models .
Using modern tools and technology provided by forward-thinking device management solutions, this isn’t a theoretical conversation, either. With tools like distribution pipelines, testing environments and staged software updates, AI model distribution can become a non-issue. This frees your development team to work on future updates, your IT team to move with agility and your business to focus on what’s important.