Solve Kaggle's Corona-Hack Challenge w/ Kubeflow, Kale and MLOps
The Kaggle Corona-Hack Chest X-Ray problem is a popular Data Science topic. In this course, you will explore how to solve this problem with Kubeflow. 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. Earn your certificate and share it on LinkedIn to show your continued progression with Kaggle, Kubeflow, and MLOps.
About This Course
This course is approximately 90 minutes long.
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 the Kubeflow PyTorch operator to orchestra PyTorch deep learning.
- Perform PyTorch deep learning as a distributed job.
- Relate the activities in this course back to the core tenents of MLOps.
In this course, you will run a PyTorch distributed training job starting from your Notebook. The Kubeflow PyTorch operator is a Kubeflow component that can orchestrate PyTorch deep learning distributed jobs. In order to describe and submit such a job, you would normally need to write a PyTorchJob CR (YAML file), providing run configurations and the containers with which the Pods will run. Besides writing this YAML file, you would also need to build a Docker image for the Pods and make sure your data is available to them as well. Fortunately, all of this now comes automated in Kale.
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 here .
Certificate of Completion
At the end of this course do not close out the course without earning and sharing your certificate! This certificate can be shared on Linkedin to showcase your new skills.
Arrikto Academy assumes that you have familiarity with popular Data Science concepts and have used some of these philosophies in practice.