More than 70% of worldwide enterprises will run more than two containerized applications in production by 2023. And Kubernetes utilization will continue to grow as companies deepen their commitment to containerization. This suggests a significant relationship exists between the adoption of Kubernetes and the rise of container and microservice architectures. It also means that people skilled in using and managing Kubernetes clusters are now in-demand across the industry.
This blog will discuss Kubernetes and how it is the ideal choice for developers to manage AI/ML production workloads.
What is Kubernetes?
Kubernetes. It is a portable, expandable, open-source platform that makes declarative configuration and automation possible for managing containerized workloads and services. It was designed originally by Google, but the Cloud Native Computing Foundation now maintains the project. It excels in running production workloads at scale with best-of-breed ideas and practices from the community. Kubernetes combines over 15 years of Google’s experience and has a large, rapidly growing ecosystem with the Kubernetes services, support, and tools being widely available.
Let’s understand Kubernetes with the help of an example. Containers are an excellent way to bundle and run your applications effectively. To prevent downtime in a production environment, you must manage the containers that run the apps. For instance, if one container fails, another one needs to start. Wouldn’t it be simpler if a system dealt with this behavior?
In this way, Kubernetes saves the day! You have a robust framework to execute distributed systems. Kubernetes handles application scaling and failover, offers deployment patterns, and more. It can also efficiently handle a canary deployment for your system.
Containers and Kubernetes – Now Widespread in The Enterprise
Containers have been the de facto norm for application development in the public cloud for several years. Due to the rise of Kubernetes, a container orchestration platform, containers are becoming increasingly popular in private clouds.
Until recently, conflicting standards had complicated the orchestration process and slowed container adoption. But during the past two years, Kubernetes has taken the lead and is swiftly overtaking all other container orchestration systems in popularity. Anyone running containers is free to use since it is open source, whether in private on-premises clouds or public clouds.
Enterprise IT departments are now aware of the advantages of containers due to the industry’s decision to use a single orchestration solution. Microservices promote efficiency and correspond with more resilient programming techniques, which enables developers to produce applications more quickly. This translates to speedier application development and better market responsiveness for enterprises.
How Kubernetes Addresses Data Science Challenges
Developers have welcomed containers and the Kubernetes ecosystem because of its capacity to decouple the infrastructure layer from modern distributed applications. Repeatability, reproducibility, portability, and flexibility are provided across various contexts and libraries by declarative deployments, continuous real-time monitoring, and dynamic service routing.
The following Kubernetes capabilities take care of many of the essential needs of workloads for data science:
- Reproducibility throughout a complicated pipeline: The training, testing, and deployment of models are only a few of the processes that make up a machine/deep learning pipeline. Kubernetes enables the research and operations teams to reliably share a combined pipeline independent of infrastructure.
- Repetition: Deep learning and machine learning are very iterative processes. Data scientists may repeat experiments with Kubernetes while having complete control over all environmental factors, such as data sets, ML libraries, and infrastructure resources.
- Portability across production environments: The capacity to move fluidly and dynamically between development, staging and production environments is a feature of Kubernetes-based containerized applications.
- Flexibility: Kubernetes offers the orchestration, deployment, and communications fabric necessary for packaging ML-based apps as highly modular microservices that can combine and match various languages, libraries, databases, and infrastructures.
Successful Kubernetes Architecture for AI Workloads
It is hardly surprising that Kubernetes has emerged as the de facto container orchestration standard for data science teams, given all the above benefits. Here are some guidelines for running data science workloads on Kubernetes to their full potential.
Kubernetes Monitoring
To right-scale Kubernetes applications in production and to maintain system availability and health, Kubernetes clusters must be monitored. However, older technologies designed for monolithic workloads cannot effectively monitor distributed, event-driven, and dynamic Kubernetes applications.
Currently, Prometheus, an open-source monitoring system with its own declarative query language, PromQL, is the industry standard for monitoring Kubernetes ecosystems. In the Kubernetes environment, a Prometheus server can find Kubernetes services and collect their metrics into a scalable time-series database. The key-value pair-based multidimensional data model used by Prometheus and the labels used by Kubernetes to organize infrastructure metadata are complementary.
The Prometheus metrics are easily accessible via API requests by dashboard-building and visualization applications. They are released via the industry-standard HTTP protocol and are human-readable. By presenting the outcomes of PromQL queries executed on the combined time-series data as tables or graphs, Prometheus itself offers fundamental visualization capabilities. When predetermined performance thresholds are exceeded, Prometheus can also send immediate alerts to the pertinent teams.
Conclusion
For all the above reasons, Kubernetes benefits machine learning and artificial intelligence. Since it provides a more comprehensive understanding of the complete software lifecycle, it is a must-learn technology. You will be introduced to best practices for setting up, managing, and maintaining production workloads in the cloud as you learn Kubernetes. It’s an in-demand, future-proof technology, and with the correct methodology and instructions, you should have no trouble adopting it.
