Today, enterprises are busy increasing their use of cutting-edge technologies, like AI, ML, and DL, for different purposes. And there is one common question that every industry is asking, like “how will they scale their industrial AI development?” This is only the first step to reaching a final and complete solution. However, AI development scaling is not the only discussion that is going across industries.
Many startups and companies who are in their initial stage of digital transformation have questions about scaling cloud-native performance, developing AI solutions, integrating AI tech into the Cloud platforms to build intelligent cloud or AI-enables cloud applications, and containerizing cloud-native applications.
The recent cloud-native application-related survey reported that industries that have containerized cloud-native applications noticed around 84-92% improvement in their production in just one year. Therefore, the Kubernetes Adoption Rate has increased from 78% to 83% than the previous year.
In order to scale-based application types, model development must be implemented with a continuous process, which can operate on critical application aspects and proceed to prepare application model work into a public-facing deployment platform. And this deployment platform refers to the Kubernetes clusters.
This article covers information about deploying, scaling, and managing AI projects using a containerized deployment platform known as Kubernetes.
The Efficiency of Kubernetes:
Whether it’s about Artificial Intelligence or Machine learning stack, the distributed architecture of Kubernetes pairs up equally with both of them and helps them achieve application platform scalability. By seeing these technology stack’s process in previous years, it is possible to assume their immense capability growth by the end of 2021.
Companies must bear one thing in mind about tools that, they will continue to receive new updates and help businesses excel in every difficult situation.
Kubernetes is capable of accelerating the software development phase using default cloud-based solutions. At the time of accelerating the development phase, it would enable organizations to increase application usages and data to help them achieve their digital transformation.
Here, we have mentioned the best Kubernetes use cases that have helped many organizations to scale and optimize their AI project performance and reduce extra service costs.
Kubernetes Use Case 1: Data Engineering Concept for AI-Based Forecasting System
Several bars and restaurants own venue management and POS (Point of Sales) software in their places, which processes a huge amount of sale history to provide comprehensive sale insights, and during that, sometimes the software does face bottlenecks. And to solve such problems, AI is the only solution which observes data patterns of sales and predicts the evaluation process of the next event accordingly.
AI computation is not an easy task to achieve because it demands specific resources, like, initially, VMs were used in the AWS EMR cloud computing services. As we implement more services and resources into the ecosystem, the infrastructure maintenance expenses will also increase accordingly. While ML algorithms process daily sale data when it’s not in use and remain idle in the day, AI algorithms perform data processing with speed even overnight. If manual computing management is required, organizations can also implement Docker Swarm into the infrastructure and reduce infrastructure maintenance costs.
Using Kubernetes in the initial stages makes no sense, but when the number of usages and entity starts to grow, it becomes more necessary to take advanced data engineering approach to scale application performance and optimize cost investment.
Kubernetes Solves Business Tasks:
Kubernetes solves the tasks mentioned below, such as:
- Scheduling sales’ data history scripts
- A database storage system that works inside the ecosystem of Kubernetes
- Running AI scripts once the historical data collection process updates successfully.
- Implementing AI dashboard specialized APIs
- Developing AI-enabled dashboards, which reflects AI script-related outcomes.
Kubernetes can optimize real-time computing resources and scale itself automatically.
1. Optimizes Application Performance and financial Investments:
While running AI scripts in the Kubernetes ecosystem, it is proven that whenever AI computes the reciprocal logic continuously on a similar size, it would be able to perform that task faster with fewer resources in its systems rather than AWS EMR cloud service platform. On the contrary to the ERM production environment, it is evident that in Kubernetes, AI modules efficiency increase to ten times faster for the same number of events.
2. Better Reliability:
Kubernetes provides better system stability than the AWS EMR ecosystem because, on EMR, many data scripts fail due to anonymous reasons and their data insights also don’t provide valuable information.
3. Better Scalability:
On the contrary, Kubernetes is not limited to the maximum number of new venues because whenever new venues are added into its ecosystem, it would automatically add clusters and empower automated scaling, which is necessary for rapidly growing projects.
Kubernetes Use Case 2: Data Engineering for An Intelligent Video Surveillance Application
Another real-world Kubernetes deployed application is an Intelligent facial recognition computing or video surveillance software powered by autoscaling features. This system consists of the front-end, back-end queues, and AI-enabled face blurring features. Kubernetes is specifically used when it is setting up and managing application configurations as an orchestrator. At the time when video processing request approaches, Kubernetes API helps an application to auto-scale its backend features and adds more workers if needed.
Embrace Future-Ready Applications with Kubernetes:
Businesses that are shifting to AI/ML-based solutions may face significant challenges, but using Kubernetes containerized orchestration platforms, like OpenShift, organizations can solve such issues and enhance software efficiency. Many organizations utilizing these configurations have achieved a greater level of efficiency and capitalization as AI promised.
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