If DevOps is the future of software development, Kubernetes is the futuristic tool. As the software development space evolves, Kubernetes is emerging as an essential platform for handling rising complexities and anomalies. For automating business processes, greater flexibility and collaboration is demanded out of DevOps teams to excel at machine learning. By developing machine learning models on Kubernetes, businesses can seize new opportunities while augmenting productivity and application performance.
We, at Oodles, as an established Machine Learning Development Company, share a sneak peek into the Kubernetes machine learning deployment journey.
Kubernetes is an open-source, cloud-native platform for managing application containers in a production environment. It simplifies the orchestration of varied containers while automating the deployment, workflow, and scaling of the applications within.
Especially in the DevOps space, Kubernetes makes the management of complex and distributed applications and frameworks easier, sophisticated, and portable.
The evolving preferences of cloud-native container management platforms with Kubernetes reaching the top slot as visualized by CNCF.
Here’s why Kubernetes and DevOps make a perfect match made in heaven for software development lifecycles-
With Kubernetes, different application environments can familiarize, collaborate, and communicate with one another, thereby reducing operational workload and compatibility issues.
For QA testing, Kubernetes reduces config-variables while eliminating the risk of discrepancies between the system configuration of test and production environments.
The inherent clusters feature of Kubernetes enhances CI/CD pipelines by providing flexibility in shifting the same code across different environments.
In addition, a consistent development, testing, and production environment for dynamic applications make Kubernetes an ideal ecosystem for developers. While this gives an overview of Kubernetes, let’s dive deeper into Kubernetes’ ability to host advanced machine learning application environments.
Often, machine learning development environments can get messy with too many frameworks, tools, and libraries running simultaneously. From data ingestion to training at scale, setting up machine learning pipelines is an extremely difficult journey for the DevOps team.
We, at Oodles, as providers of artificial intelligence services, understand the complexities of building machine learning models from data ingestion to final roll-out.
Kubernetes streamlines machine learning pipelines by making them easily composable, portable, and scalable. With the ability to run on local machines like desktops and GPUs, Kubernetes can translate workflows across platforms effortlessly.
Here’s how a typical machine learning pipeline looks like in production-
However, the missing bugbears here are the tons of open-source tools deployed at every stage across machine learning pipelines. Some of the most prominent tools used to deploy machine learning models on Kubernetes are-
This is where Kubernetes emerges as a groundbreaking tool for machine learning projects. It provides the much-needed elasticity to run diverse frameworks under one roof.
In addition, here are some distinctive features of Kubernetes-
a) Auto-scalable infrastructure
b) Better resource utilization
c) Horizontal scaling and load balancing
d) Self-healing capabilities
e) Seamless application updates
f) Lower time to upload new applications
g) Automated scheduler launches and controls, and more.
Currently, major tech giants are offering unique Kubernetes platforms including Google’s GKE, Amazon EKS, and Azure Kubernetes Services (AKS). However, the basic features remain common for every platform, giving Kubernetes an edge over other cloud-native technologies.
At Oodles, we are a team of seasoned AI developers who are constantly evolving our software development practices. Given the expansive benefits of Kubernetes, the Oodles AI team is containerizing many of our applications and POC models.
By deploying machine learning models on Kubernetes and Docker, we ensure businesses time and cost-effective ML application development journey.
Under DevOps and cloud services, our capabilities include the following-
a) System engineering
b) CI/CD pipelines on microservices
c) Serverless computing
d) Configuration management
e) AWS services
f) End-to-end data management
g) Continuous monitoring
Connect with our AI Development team to explore our custom ML applications including document digitization, predictive analytics, and more on Kubernetes.