The Kaggle OpenVaccine problem is a popular Data Science topic. In this course, you will explore how to solve this problem with Kubeflow and Kale. In addition, you’ll learn how the work you are doing is the foundation for an effective and self-sustainable MLOps culture and platform solution that you can undertake at your enterprise.
This course is presented as a series of hands-on articles where you will learn about Kaggle, Data Science, and MLOps while using the Kubeflow platform with Kale to compile and run Kubeflow Pipelines. The overall time commitment is about 1 to 1.5 hours.
Specifically in this course, you will:
Learn about Kaggle.
Learn about Kubeflow.
Learn about MLOps.
Use Jupyter Notebooks in Kubeflow to review the Kaggle OpenVaccine Problem Solution.
Use Kale to convert a Jupyter Notebook into a Kubeflow Pipeline.
Use Katib to perform Hyperparameter Tuning on the ideal OpenVaccine model.
Load the Kubeflow Pipeline Snapshots in new Notebook Servers.
Serve the ideal OpenVaccine model from a Jupyter Notebook.
Relate the activities in this course back to the core tenets of MLOps.
Requirements: We assume that you have familiarity with popular Data Science concepts and have used some of these philosophies in practice.
Instructor-Led Option: If you would prefer to take the course live, this course is available on a monthly basis with an instructor. If this is your preference, navigate and sign up on the Arrikto events page.
