[Free] Solve Kaggle's Openvaccine Challenge W/ Kubeflow And Mlops

What you’ll learn

• Articulate the relationship between the Kaggle OpenVaccine Competition and Kubeflow.
• Outline the stages of MLOps and explain the value of Kubeflow as it pertains to MLOps.
• Use Jupyter Notebooks in Kubeflow to review the Kaggle OpenVaccine Problem Solution.
• Define Kubeflow Pipeline using Kale and Jupyter Notebooks hosted on Kubeflow Clusters.
• Use Katib to perform Hyperparameter Tuning with Kubeflow Pipelines.
• Load Kubeflow Pipeline Snapshots in new Notebook Servers to restore previous state.
• Serve the ideal model from a Jupyter Notebook.
• Articulate how Machine Learning technologies come together to support MLOps.

Requirements

• Familiarity with popular Data Science concepts

Description

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.