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How Kubernetes is Fuelling Demand for Artificial Intelligence

At the beginning of 2017, among the three major public cloud vendors, only the Google Cloud Platform supported Kubernetes.

By CBR Staff Writer

The possibilities of how Artificial Intelligence (AI)-enabled technology will enhance our lives and improve our workload is a subject constantly capturing our imaginations, writes Carmine Rimi, AI Product Manager at Canonical.

Since 2000, there’s been a 14-times increase in the number of AI startups, according to a Stanford University study, while in the UK, AI developers saw a venture capital funding increase of more than 200 per cent in the past year.  Its potential to change business is seemingly limitless.

However, creating these applications to improve our lives and businesses is no mean feat. In fact, AI-enabled applications are extremely hard to build. Large AI programs are difficult to design and write as they involve many types of data. Porting them from one platform to another tends to be troublesome.

What’s more, several steps are required at each stage to begin building even the most rudimentary AI application, each requiring different skills. Feature extraction, data collection verification and analysis, and machine resource management make up the majority of the codebase necessary to support a comparatively small subset of actual ML code.

Keeping it Contained

There’s a lot of work to do to get to the starting line, as well as a large amount of ongoing effort required to keep the applications current. Fortunately, Kubernetes — the open-source platform that automates the deployment and management of containerised applications, including complicated workloads like AI and machine learning — can be a facilitator.

Containers provide a compact environment for processes to run. They’re easy to scale, they’re portable across a range of environments – from development to test to production – and they therefore enable large, monolithic applications to be broken into targeted, easier-to-maintain microservices.

Kubernetes has had a meteoric rise as a container orchestration platform. The vast majority of respondents to a Cloud Native Computing Foundation survey say they are increasing their use of Kubernetes across a variety of development stages. Forrester stated recently that “Kubernetes has won the war for container orchestration dominance and should be at the heart of your microservices plans.”

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The Go-to for AI

Kubernetes and AI represent converging trends. Most companies are running (or plan to move to) Kubernetes as a platform for their workloads, and AI is an increasingly important workload.

Kubernetes is ideal for the job because AI algorithms must be able to scale to be optimally effective. Some deep learning algorithms and data sets require a large amount of compute. Kubernetes helps because it is all about scaling based on demand.

It also provides a way to deploy AI-enabled workloads over multiple commodity servers across the software pipeline while abstracting away the management overhead. Once the models are trained, serving them in various deployment scenarios, from edge compute to central datacenters, poses challenges to non-containerised forms of applications. Again, Kubernetes can provide the necessary flexibility for a distributed rollout of inference agents on a variety of substrates.

Shifting Attention

As organisations increasingly shift their attention to AI to decrease operating costs, improve decision-making and serve customers in new ways, Kubernetes-based containers are becoming the go-to technology to help enterprises adopt AI and machine learning. In December of last year, the Kubernetes project introduced Kubeflow, “dedicated to making deployments of machine learning workflows on Kubernetes simple, portable and scalable.”

While Kubernetes started with just stateless services, the project said, “customers have begun to move complex workloads to the platform, taking advantage of rich APIs, reliability and performance provided by Kubernetes. One of the fastest growing use cases is to use Kubernetes as the deployment platform of choice for machine learning.”

At the beginning of 2017, among the three major public cloud vendors, only the Google Cloud Platform supported Kubernetes, with its Google Kubernetes Engine. By the end of the year, they were all on board, after Microsoft added Kubernetes support to the Azure Container Service and Amazon debuted the Amazon Elastic Container Service for Kubernetes. Now, the uses for Kubernetes and ways it’s being deployed by companies is seemingly limitless.

That’s a lot of activity in a relatively short time span, and it shows the extent to which tech vendors and their customers are rallying around the notion that containers offer huge benefits in developing and managing AI components of applications.

The rise of AI is helping fuel the growing interest in containers as an excellent way to bring repeatability, fault tolerance and repeatability to these complex workloads. And Kubernetes is becoming a de facto standard to manage these containerised AI applications. It’s a wonderful match that should dramatically benefit enterprises for some time to come.

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