Despite economic anxiety brought on by the 2020 pandemic, investors chose to put their money into artificial intelligence development at a higher rate than in the previous year. Overall, 2020 investment in AI startups exceeded 40 billion US dollars, with an increase of 9.3% from 2019 numbers, according to the Artificial Intelligence Index Report 2021 by Stanford University.
Rapid growth required rapid changes in AI development, deployment, staging, and integration with other platforms. Organizations turned to containerized applications orchestrated by the Kubernetes platform to scale efficiently and move data across machines.
Google’s open-sourced Kubernetes platform provides software engineers and developers the opportunity to launch containerized applications in a managed environment across the application’s life cycle. In addition, developers can expand containerized applications across machines.
Since 2015, developers have used Kubernetes to manage containerized apps with greater availability and scaling capabilities than non-containerized apps.
Containerized apps are easily scalable and readily accessible due to their independent design.
Containerized apps are designed to “think” they have their own operating system, even if the operating system is shared with multiple other containerized apps. Since the app is an independent component, it can be reused, moved, and redistributed as needed, without interfering with other containerized apps on the same operating system.
Kubernetes orchestrates various apps so that computing resources are optimally managed and uneven loads are automatically redistributed. Through Kubernetes, containers can be deployed, managed, scaled, and integrated across platforms as needed.
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AI systems depend on many moving parts. AI engineers require an intricate IT environment and an array of digital tools to build AI-powered applications. AI computations also require extensive infrastructure management. Infrastructure costs, data management, and multiple tools create expensive IT environments that are difficult to scale.
Kubernetes is a solution to these inherent problems. As described earlier, containerized apps can be managed automatically using Kubernetes. Smaller pieces of infrastructure can be scaled, redistributed, and integrated as needed without having to redesign an entire IT environment.
In addition to the digital environment required to support AI systems, there are logistical constraints that Kubernetes can mitigate.
AI projects that reside in the cloud might be locked into a specific vendor. The vendor may not have the resources needed to scale and accommodate growing AI projects. In a scenario like a downtime, technical incompatibilities and legal restraints are beyond the project engineer’s control, yet these issues can impact the user experience.
Kubernetes fulfills logistical demands and provides users with a stable environment because of its open-source platform structure that accommodates complex workloads. After all, when AI algorithms are not able to scale they cease being effective.
Kubernetes provides the ability to meet scaling needs in both the logistical sense as well as the technical aspect.
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It is no coincidence that AI growth has paralleled Kubernetes’ growth. In 2020, The Cloud Native Survey reported that the use of containers in production has increased from 84% in 2019 to 92% in 2020. In addition, Kubernetes’ production use has increased from 78% in 2019 to 83% in 2020.
Back in 2018, Gartner predicted that AI development might undergo a period of slow growth. Gartner stated, “…through 2020, 80% of AI projects will remain alchemy, run by wizards whose talents will not scale in the organization.”
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Fortunately, an inability to scale has not impacted every AI project that underwent development and growth in 2020. An example of Kubernetes’ and AI’s success can be seen in the example of AI-powered video surveillance.
A video surveillance system includes an AI-based feature for video processing on top of its basic components: WebRTC video streaming, front end, and back end.
The deep learning based video processing feature is used for facial detection and recognition. During the COVID crisis, facial detection was an important live stream feature as it could identify whether or not individuals were masked. It was also used for thermal screening. Using a process of decoding followed by AI computation and then decoding, the AI video processing unit required a great deal of computing resources.
Computing resources were further tested by the project’s livestreaming aspect. The load curve varied greatly depending on the time of day, week, and season. Kubernetes API’s back end acted as an Orchestrator by auto-scaling the number of machines necessary to process a request. Without additional manual resources, Kubernetes API automatically provided computing resources optimization.
The unprecedented conditions of 2020 called for increased optimization of AI tools and development processes. Kubernetes was able to fill the need using a cloud-native ecosystem. The pace of development increased thanks to a modernized platform. In combination with AI applications and employee skills, Kubernetes can adjust to shifts in your industry’s landscape.
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