Solve Kaggle's OpenVaccine Challenge w/ Kubeflow and MLOps

Solve Kaggle's OpenVaccine Challenge w/ Kubeflow and MLOps, available at Free, has an average rating of 4.45, with 14 lectures, based on 18 reviews, and has 1017 subscribers.

You will learn about 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. This course is ideal for individuals who are Anyone interested in learning more about Kaggle, Kubeflow and / or MLOps It is particularly useful for Anyone interested in learning more about Kaggle, Kubeflow and / or MLOps.

Enroll now: Solve Kaggle's OpenVaccine Challenge w/ Kubeflow and MLOps

Summary

Title: Solve Kaggle's OpenVaccine Challenge w/ Kubeflow and MLOps

Price: Free

Average Rating: 4.45

Number of Lectures: 14

Number of Published Lectures: 14

Number of Curriculum Items: 14

Number of Published Curriculum Objects: 14

Original Price: Free

Quality Status: approved

Status: Live

What You Will 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.

Who Should Attend

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

Target Audiences

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

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.

Course Curriculum

Chapter 1: Kaggle, Kubeflow and MLOps Overview

Lecture 1: Kaggle Open Vaccine Competition

Lecture 2: What is MLOps and Why Important

Lecture 3: Why Kubeflow and MLOps

Lecture 4: Why Kubeflow as a Service for Kaggle & MLOps

Lecture 5: (Hands-On) Kubeflow as a Service

Chapter 2: Create the OpenVaccine Model

Lecture 1: (Hands-On) Load the Notebook in a Kubeflow Cluster

Lecture 2: MLOps & the Model Development Life Cycle

Lecture 3: (Hands-On) Compile and Run Kubeflow Pipeline from Notebook

Lecture 4: MLOps & Kubeflow Pipelines

Chapter 3: Tune & Serve the OpenVaccine Model

Lecture 1: (Hands-On) Hyperparameter Tune the OpenVaccine Model

Lecture 2: (Hands-On) Restore Notebook from a Pipeline Step

Lecture 3: (Hands-On) Serve the Model from Inside the Notebook

Chapter 4: MLOps Moving Forward

Lecture 1: Marching Towards MLOps Utopia

Lecture 2: MLOps @ Arrikto

Instructors

Alexander Aidun
Educating the masses on Cloud Technologies
Ben Reutter
Media lead at Arrikto

Rating Distribution

1 stars: 0 votes
2 stars: 1 votes
3 stars: 0 votes
4 stars: 7 votes
5 stars: 10 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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