Artificial intelligence receiving huge Kubernetes boost
by Anasia D'mello
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There has been a 14-times increase in the amount of Artificial Intelligence (AI) start-ups launching since the turn of the century, according to a study by Stanford University. In the UK alone, says Carmine Rimi, AI product manager at Canonical – the company behind Ubuntu, AI developers witnessed a 200% spike in venture capital funding in the past year alone; as the transformative potential of AI smashes all boundaries.
The creation of AI applications to enhance ways of doing business and, indeed, people’s lives is a huge task. These applications are complicated to develop and build, as they involve such varying types of data; making porting to different platforms troublesome.
Above these challenges, several steps are needed at each stage to start constructing even the most basic AI application. A spectrum of skills is necessary, including feature extraction, data collection verification and analysis, and machine resource management, to underpin a comparatively tiny subset of actual ML code. A lot of work needs to happen before taking a position on the start line; alongside a large amount of ongoing effort to keep the applications up to date. All developers are searching for ways to beat these big challenges.
Contain yourself
The result of this search, to keeps apps up to date and balance workloads in app development, often comes to the same answer – Kubernetes. This open source platform can be a facilitator, as it can automate the deployment and management of containerised applications, comprising complicated workloads such as AI and Machine Learning. Kubernetes has enjoyed something spectacular because its capable of these things, but also as a container orchestration platform.
Forrester recently stated that “Kubernetes has won the war for container orchestration dominance and should be at the heart of your microservices plans”. Containers deliver a compact environment for processes to operate in. They are straightforward to scale, portable on a range of environments and they, therefore, enable large, monolithic applications to be split into targeted, easier-to-maintain, microservices. The majority of developers say they are leveraging Kubernetes across a variety of development stages, according to a Cloud Native Computing Foundation survey.
Most companies are running, or plan to start using, Kubernetes as platform for workloads. Of course, AI is a workload that is rapidly garnering importance. Kubernetes is ideal for this task, because AI algorithms must be able to scale to be effective. Certain deep learning algorithms and data sets need a large amount of compute. Kubernetes can help here, because it is focused on scaling around demand.
Kubernetes can also provide a roadmap to deploying AI-enabled workloads over multiple commodity servers, spanning the software pipeline, while abstracting out the management overhead. After the models are trained, serving them in differing deployment scenarios, from edge compute to central datacentres, is challenging for non-containerised application forms. Once again, Kubernetes can unlock the necessary flexibility for a distributed deployment of inference agents on a variety of substrates.
Changing focus
As businesses move their attention to AI to slash operating costs, improve decision-making and cater for customers in new ways, Kubernetes-based containers are rapidly becoming [...]
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