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
Data Science on Kubernetes I Image source - Ubuntu 

The increasing popularity of deep learning, a subset of Artificial Neural Networks, is due to its superior accuracy in data science, even when dealing with large datasets.

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

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

Data science workloads require flexible and highly parallel processing, and the term that best encapsulates these needs is MLOps. Kubernetes makes it easy to develop, deploy, and manage the best open-source systems for ML on diverse infrastructures, making it an invaluable tool for resource-intensive MLOps workflows with multiple stages and varied requirements.

Data science on Kubernetes

Kubernetes is an orchestration tool that manages underlying infrastructure, running consistently across different environments with features to package each step of the process as a container, making it portable and modular.

Kubernetes enables data scientists to quickly spin up new instances when heavy computational loads arise and then shut them down as needed to meet the requirements 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 allowing data practitioners to scale as needed to handle variable demand," said Nucleus Research1 Senior Analyst Alexander Wurm. This empowers data scientists to test for scenarios they would otherwise not have been able to carry out. "Additionally, the market is now saturated with managed Kubernetes services to automate administration tasks and simplify the deployment of data science workloads," Alexander mentioned.

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 is 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. It's known as CI/CD/CT. The main point is to deliver Data Science to production in the right way and in a scalable way, 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.2

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

  • Model building and training — Kubeflow provides Jupyter instances, so we can experiment and develop our models. Kubeflow also can automate hyperparameters 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, 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 practice can be complex and demanding, requiring the collaboration of professionals from different disciplines and backgrounds to embrace the practice of information engineering.

The approach that combines data science with the benefits of being Kubernetes-native is becoming increasingly popular, with companies seeking data scientists who can drive "automation and containerization strategies," as well as 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.

— Open source training

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.

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

  • Kubernetes certification programs can help to bridge the gap in knowledge and expertise by providing individuals with the skills and understanding to work with Kubernetes for ML.
  • MLOps specialty training can help to further enhance the understanding of Kubernetes and its capabilities. MLOps courses from notable organizations provide an in-depth look at the various stages of the MLOps workflow, such as data collection, model development, deployment, and monitoring.
  • Open source—software developer certifications and training programs from the Linux Foundation can provide individuals with advanced skills and understanding to work with open source technologies, such as Linux, Kubernetes and Node.js.

The education programs should be implemented to equip developers/engineers with the knowledge needed to manage and use Kubernetes, ultimately enabling data science teams to develop and deploy machine learning models in an efficient, effective, and scalable way.


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 Kubeflow documentation to learn how you can leverage Kubeflow to enhance your Data Science capabilities.
Recommended read — The State of Kubernetes Report: Overprovisioning in real-life containerized applications
  1. Nucleas Research
  2. Argo Saakyan

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