AI, ML, Cloud and Edge Compute: What They Can Mean for Distributors

Artificial intelligence, machine learning, edge compute and 5G technology are poised to play key roles in distributor technology transformation.
3d rendering circuit cloud on tablet for cloud computing technology

By embracing digital technologies such as artificial intelligence, machine learning and cloud computing, distributors can be better prepared to shift assets on demand while also improving customer experience, reducing costly onsite hardware and enabling faster response times.

On June 29, Roy Chua, founder and principal of independent research and advisory service AvidThink, will dive into the benefits of AI, ML, the cloud and edge compute, and what they bring to distributors during MDM’s Digital Distributor Summit. Chua also talks about digital technologies in this week’s MDM Podcast.

How cloud powers AI/ML

AI and ML have been in use for many years by large hyperscale cloud providers such as Amazon Web Services, Google Cloud Project, Microsoft Azure, Alibaba, Tencent, Baidu, IBM/Red Hat and Oracle. AI and ML are part and parcel of those service providers’ cloud offerings, which have extended beyond large enterprises and telecommunication companies into a variety of medium and even small-sized businesses.

The cloud “enables new development processes, new application hosting and new service delivery to customers because of the scale and the ability to process data more efficiently,” Chua says in the podcast.

AI and ML are the domains of the cloud providers because they have been using them internally for years. (Without AI and ML, Google wouldn’t be able to process billions of search requests every day.) Now, they are offering those same benefits to distributors, enterprises and telecommunications providers.

Edge compute delivers SaaS apps closer to end customers

In addition to AI/ML and the cloud, edge compute has also come into play over the past few years as those clouds — and the software-as-a-service applications that they host, such as Salesforce or Microsoft 365 — have moved closer to the end users. By moving the clouds closer to the ‘edge,’ customers have access to their services and applications with much lower latency. In the centralized cloud model, a distributor’s software application would run through a data center, or several data centers, to the cloud and then back. By being at the edge near the customer, latency can be reduced.

Having those same resources closer to the edge increases the speeds at which applications can be used. This comes in handy for robots, autonomous vehicles or IoT sensors in a warehouse or manufacturing floor. Edge compute includes a shared pool of configurable computing resources such as network servers, storage, applications and services. By having those in an edge location, distributors no longer need to buy and maintain expensive on-premise equipment, such as routers.

Cloud and edge compute free-up distributors’ IT departments to focus on other areas instead of monitoring and troubleshooting operations, networks and supply chains. Clouds and edge compute also allow companies to comply with local state, or even nation data residency requirements by keeping data within the required boundaries. For the most part, cloud offers better security than running applications or services over the public internet.

“Fundamentally, there’s a range of clouds that are no longer centrally located. There are clouds in the metropolitan area and there’s even local clouds, or edge clouds,” says Chua. “They all can power AI and ML. Picking the right one depends on the cost element and the performance element.”

In addition to AI, ML, cloud and edge compute, Chua will also speak at MDM’s Digital Distributor Summit about how public and private 5G networks can be used by distributors for industrial IoT deployments to track data across supply chains while providing more robust connectivity in warehouses and manufacturing facilities.

Meanwhile, listen to the full podcast with Chua, below.

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