Leading enterprise technologies such as hybrid clouds, and containers are driving digital change Artificial intelligence. Acceleration is added 5G, which adds decentralization information and application processing from millions of endpoints outside the traditional data center and audience cloud.
About the author
Sam Werner is Head of Bid Management, IBM Systems.
But while change and modernization increase a company’s performance, efficiency, and agility, these increasingly complex infrastructures also make it difficult to manage and access information, especially in artificial intelligence workloads. For example, capturing data from the edge of the network, not to mention data from external sources, usually means transferring and copying data – a process that is not only time consuming and expensive, but also brings new levels of risk, management and safety challenges.
Today, one of the few ways to circumvent this challenge is to invert the equation and push artificial intelligence into data, the other way around. But it’s easier said than done. To do that and implement comprehensive data aggregation, companies need a fundamental data warehousing layer that is in containers and supports hybrid clouds.
Feeding the AI beast
We have an internal strategy we call the “AI Ladder”. It is the framework for a successful implementation that identifies four critical “steps”: data collection / collection, data organization, analytics, and infusion throughout your company. At its core is a fundamental data warehouse layer, a fabric that serves every bus and allows companies to perform their artificial intelligence literally anywhere, in any environment: on-site, in a public cloud, in a private cloud, and on the edge. This is because this layer can synthesize high-performance and high-capacity systems using container-based management software and the Red Hat OpenShift Orchestra.
By braiding container software into fabric, it allows data services to be built and run through hybrid clouds easily and quickly and all in one code base. As a result, things that were considered impossible just a few years ago are not only possible, but can be standard – for example, global access to information, cooperation, data flexibility and security.
Now consider single-layer software-defined storage that works on-premises, in the cloud, and on the edge. This software-defined storage infrastructure can now provide a single, global namespace that connects the edge, data center, and public cloud infrastructure into a single data lake. When such an infrastructure is in place, applications have access to the same data no matter where they are, and all have one copy of the data. Together with data management software to manage policies and permissions, you can eliminate the need to make expensive copies, which in turn reduces the risk of compliance exposure, such as GDPR, security threats, and data breaches. It also provides a single source of truth for artificial intelligence workloads, avoiding confusion over data vintages.
Dumping of the foundation
This basic data storage layer is container-centric to support hybrid clouds and global data availability; it also acts as the underlying machine so that artificial intelligence can be deployed anywhere. With this level of knowledge, companies can write their code once and use it anywhere.
This is not a utopian view of the company. It’s here now. At IBM, we’ve helped businesses large and small set up their data storage operations to support hybrid clouds and then expand the AI ladder. But not all data and artificial intelligence or cloud service providers feel the same. For some, it’s simply easier to lock your business into their artificial intelligence in the cloud instead of taking the opposite approach – and allow companies to use your clouds to reach other frameworks.
Locking up a cloud provider is a real thing and can earn information or make it openly available. The basic container, built with tanks and a clear path to the hybrid clouds, becomes a line of “modified” IT infrastructure. And the impact and significance of these data or data architectures are at the heart of artificial intelligence. We want to say that there is no artificial intelligence without IA. And this has never been more truthful than it is today in 2021.