Why Kubeflow as a Service for Kaggle & MLOps?

MLOps is made possible with a technical foundation built upon Kubeflow. Kubeflow is a Data Scientist obsessed platform that leverages the power of Kubernetes to improve the Model Development Life Cycle by abstracting away the K8s complexity. Simply put, Kubeflow makes it easier for Data Scientists to focus on data science. Using the Kubeflow platform reduces the friction that Data Scientists and MLOps professionals face daily, allowing for greater collaboration and reducing model time to production. Migrating to Kubeflow, and paying down your technical debt, introduces long-term stability, increased reusability,  improved quality, and decreased future maintenance.

As a reminder, an effective and self-sustainable MLOps culture and environment built upon Kubeflow using Kubeflow Pipelines is characterized by:

  1. An abstracted methodology to interface with specialized services to get Model Development Life Cycle work done efficiently in a decoupled manner.
  2. The ability to continuously build, train and improve models to ensure stability and accuracy during production.
  3. Strategies and processes to repeatedly transform raw datasets, produce predictive features, and respond to business goals.
  4. An environment that handles the life cycle of continuous training pipelines and resulting models as well as monitoring the quality of prediction result.

All in all, MLOps is a unified vision and automated Continuous Training process which cycles through points 1 - 4 above to improve the model in response to ever-evolving business needs. This approach, once adopted, maximizes both the velocity of model development as well as the resilience and reliability of the overall system. The reduction in friction/toil helps organizations achieve a reduction in hours committed to work that is manual, repetitive, and has no intrinsic value to the organization.

Individuals interested in exploring Kaggle challenges or even solving new ones should start with Kubeflow since it is the fastest and best way to quickly iterate on a model for any problem presented in a Kaggle Competition. While there are multiple ways to solve this problem,  none will provide the same foundation for a MLOps practice and culture as Kubeflow. Fortunately for the Kaggle community, the data provided and used is already consolidated,  cleansed, and transformed. Typically this work would need to be done by a Data Engineer in advance of any Data Science work, however, that step will be skipped thanks to the work of the community. Therefore as you work through this course keep in mind that you are already one step into the Model Development Life Cycle.

This course and the hands-on activity will all take place in Kubeflow as a Service where you will have access to a full Kubeflow deployment in a single click! Kubeflow as a Service is the fastest way to get up and running with Kubeflow and take your first step towards an MLOps culture and environment for your enterprise. Throughout this course, we will continue to acknowledge how the activities you are performing in Kubeflow as a Service relate to and reflect on what will happen as you migrate to a full MLOps environment. As you proceed beyond this course in your MLOps journey you will continue to use Kubeflow as a Service, until you move to a fully managed Enterprise Kubeflow as a Service or another equivalent type of deployment.