Why Kubeflow for MLOps

Kubeflow is a Data Scientist obsessed platform that leverages the power of Kubernetes to improve the Model Development Lifecycle by abstracting away the K8s complexity so Data Scientists can focus on data science. Kubeflow is a specialized ML platform that is built for Kubernetes and runs in Kubernetes clusters as a collection of Pods and Operators. Kubeflow harnesses the power of Kubernetes to orchestrate containerized environments allowing enterprises to optimize the path from development to production. Kubeflow provides a framework for Data Scientists to run ML services and workloads via IDEs, such as JupyterLab Notebooks, and serve these models using native Kubeflow functionality or by directly integrating with existing CI / CD processes.

Kubeflow also provides out-of-the-box lineage tracking of the data artifacts used to create models as well as the exact model creation process. This is critical for auditing, reproduction, and governance of the environment and acceptance of models into production environments. Using the Kubeflow platform reduces the friction that Data Scientists and other MLOps professionals face daily, allowing for greater collaboration, and reducing model time to production. Migrating to Kubeflow, or managing and paying down your technical debt, introduces long-term stability, increases reusability, improves quality, and decreases future maintenance. This first step is the most crucial because you are choosing to invest in your future technology stack.

From a technical perspective, Kubeflow components run within the Kubernetes Pods. The Pod is the main unit of scheduling in Kubernetes, it runs containers and can request specific compute resources. A pod may request K8s to provision a large pool or memory, a huge data volume, or a GPU. These requests happen behind the scenes and the data scientist doesn’t have to worry about these compute resources finding their way into their workloads. Notable Data Science centric workloads that run within Pods are Notebook Servers, Kubeflow Pipeline steps, distributed jobs, and served models. These workloads assume K8s will seamlessly schedule them across the cluster nodes based on their compute requirements thus improving efficiency and resilience. Kubeflow and Kale allow data scientists to focus only on model development while letting the underlying components deal with the provisioning and scheduling of resources. Once you’ve made this choice you are on an exciting journey along with the Open Source community and the large number of enterprises that have selected Kubeflow as their MLOps platform. As a result of Kubeflow’s obsession with Data Science, it is the fastest and best way to quickly iterate on a model. While there are multiple ways to solve this ML problem, none will provide the same foundation as Kubeflow. Choosing Kubeflow as your platform establishes the foundation for a robust MLOps environment in support of a long-term production solution. Therefore as you explore MLOps you will do so using Kubeflow and the associated technical components that make the Model Development Life Cycle possible and set the foundation for Continuous Testing, Integration, and Deployment.