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Solve Kaggle's OpenVaccine Challenge w/ Kubeflow and MLOps
Rating: 4.1 out of 5(25 ratings)
1,458 students

Solve Kaggle's OpenVaccine Challenge w/ Kubeflow and MLOps

Data Science, Kubeflow, Kale and MLOps come together in this course based on the Kaggle OpenVaccine Challenge;
Created byAlexander Aidun, Ben Reutter
Last updated 8/2022
English

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.

Explore related topics

Course content

4 sections • 14 lectures • 41m total length

  • Kaggle Open Vaccine Competition1:24

    Objective: Articulate the relationship between the Kaggle OpenVaccine Competition and Kubeflow

  • What is MLOps and Why Important7:22

    Objective: Articulate MLOps and explain the value of MLOps to growing Data Science and Machine Learning teams.

  • Why Kubeflow and MLOps3:19

    Objective: Articulate the intersection of MLOps and Kubeflow and the significance of Kubeflow for MLOps.

  • Why Kubeflow as a Service for Kaggle & MLOps3:25

    Objective: Prepare to use Kubeflow as a Service with Kale for MLOps.

  • (Hands-On) Kubeflow as a Service0:40

    Objective: Set up your Kubeflow as a Service environment for the Hands-On portions of the lecture.
    Hands-On Steps: ~ 5 minutes

Requirements

  • Familiarity with popular Data Science concepts

Description

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.    

Who this course is for:

  • Anyone interested in learning more about Kaggle, Kubeflow and / or MLOps

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