Kubernetes for Data Science Practice

Data science has exploded as a practice in recent years, with open-source technologies such as Linux and Kubernetes playing a major role in driving innovation.

Kubernetes for Data Science Practice
Image by Mariah Dalusong / Unsplash

The increasing popularity of deep learning, a subset of Artificial Neural Networks, can be attributed to its superior accuracy in data science, particularly when handling large datasets.

Deep learning functions like a powerful microscope, enabling us to uncover hidden layers of data that were previously inaccessible. However, the computational requirements for data science projects are often complex and resource-intensive.

A common approach for data scientists involves writing a model in Python and utilizing open-source libraries such as TensorFlow or PyTorch. Nevertheless, they may encounter difficulties and computational complexities when attempting to create the model necessary for training, deploying, and managing the project.

Data science workloads necessitate flexible and highly parallel processing, which is best encapsulated by the term MLOps. Kubernetes simplifies the development, deployment, and management of the best open-source systems for ML on diverse infrastructures. As a result, it is an invaluable tool for resource-intensive MLOps workflows with multiple stages and varied requirements.

Kubernetes for Data Science

Kubernetes is an orchestration tool that manages underlying infrastructure, ensuring consistent operation across different environments by packaging each step of the process as a container, making it portable and modular.

Kubernetes allows data scientists to swiftly spin up new instances when faced with heavy computational loads and shut them down as needed to meet the demands of analytical workloads.

"The value of Kubernetes for data science lies in its ability to run workloads on various platforms, including on-premises infrastructure, public clouds, and hybrid environments, while enabling data practitioners to scale as needed to handle variable demand," says Nucleus Research Senior Analyst Alexander Wurm. This capability empowers data scientists to test scenarios they would otherwise not have been able to execute. "Additionally, the market is now saturated with managed Kubernetes services to automate administrative tasks and simplify the deployment of data science workloads," he adds.

Kubeflow for Data Science Workflows (MLOps)

Kubeflow is a platform for running machine learning (ML) workloads on Kubernetes, designed to simplify the development, training, and deployment of ML models.

The process that goes into the MLOps workflow has many stages, and each stage is made up of different building blocks you choose. These different stages have varying requirements, and Kubeflow pipelines can help address the challenges that arise.

Kubeflow pipelines are automatically deployed with framework support for execution monitoring, workflow, scheduling, metadata logging, and versioning.

Kubeflow pipelines are used in data science to automate deployment and updates with continuous integration, deployment, and training, known as CI/CD/CT. The main point is to deliver Data Science to production in the right way and in a scalable manner because the main issue in Data Science is that everyone can do it in a Jupyter Notebook, but deploying it in the right way to production is not that common. "Kubeflow allows us to do all of that," explained Computer Vision researcher, Argo Saakyan, with supplementary comments.

Kubeflow enables data scientists to codify their ML workflows so that they are easily composable, sharable, and reproducible. Let's delve a little deeper into that:

  • Model building and training — Kubeflow provides Jupyter instances, so we can experiment and develop our models. Kubeflow also can automate hyperparameter tuning.
  • Pipeline creation — This is where we take our raw data and push it through the pipeline to do all the preprocessing, so our data is always ready for our models.
  • Model serving — With support for multiple libraries like PyTorch, TensorFlow, scikit-learn, XGBoost, and TensorRT, we can use a serverless solution for model deployment. This is an auto-scalable solution, where when we need more horsepower, we get more of it. We don't have to manually manage the number of servers for that, because a serverless solution does it automatically.
  • CI/CD — This connects everything and helps you deliver new models quickly. Your data goes through the pipeline, the model is trained and validated, then it is deployed, and your users can now take advantage of your service.

Overall, Kubeflow and Kubeflow Pipelines can help simplify and automate the process of developing, deploying, and managing ML models in production, saving time and effort for data scientists and ML engineers.

Kubernetes Adoption

The adoption of Kubernetes for data science practices can be complex and demanding, requiring collaboration among professionals from various disciplines and backgrounds to embrace information engineering practices.

The approach that combines data science with the benefits of being Kubernetes-native is becoming increasingly popular. Companies are seeking data scientists who can drive "automation and containerization strategies" and collaborate with Data Engineers, Cloud DevOps Engineers, DevOps Engineers, and Solutions Architects to maximize the use of Kubernetes. This may involve cultivating open-source talent, as well as teaching best practices and methodologies for managing ML models on Kubernetes.

Closing the Skills Gap

The adoption of Kubernetes is increasing, and it is highly effective for managing data science workloads, including traditional data analytics applications. However, there are some obstacles to Kubernetes adoption, such as a lack of in-house expertise.

Kubernetes is a complex system that requires a high level of familiarity and expertise to work with service endpoints, immutable deployments, persistent volumes, scaling, GPUs, packaging, APIs, etc. Therefore, open-source training is essential for equipping teams with the critical skills and understanding needed to work with Kubernetes and its features for data science workflows.

  • Kubernetes Certification programs can help close the skills gap in knowledge and expertise by providing individuals with the specialized skills and fundamental understanding required to work with Kubernetes for ML.
  • Software Developer Certification programs in the open source domain from the Linux Foundation can provide individuals with advanced skills and understanding to work with OSS technologies, such as Linux, Kubernetes, and Node.js.

Companies typically implement programs to equip developers and engineers with the knowledge required to manage and utilize Kubernetes, ultimately enabling data science teams to develop and deploy machine learning models efficiently, effectively, and in a scalable manner.

Closing Note

Kubernetes is a powerful orchestration tool that provides infrastructure abstraction for data science workloads, making it easier to quickly spin up new instances when heavy computational loads arise.

Kubeflow simplifies and automates the development, deployment, and management of ML models in production, saving time and effort for data scientists and ML engineers.


  • Visit the Kubeflow documentation to learn how you can leverage Kubeflow to enhance your Data Science capabilities.