Development » Solve Kaggle’s OpenVaccine Challenge w/ Kubeflow and MLOps
Free Solve Kaggle s Openvaccine Challenge W/ Kubeflow And Mlops
Data Science, Kubeflow, Kale and MLOps come collectively on this course primarily based on the Kaggle OpenVaccine Challenge – Free Course
Development
What you’ll study
Articulate the connection between the Kaggle OpenVaccine Competition and Kubeflow.
Outline the levels of MLOps and clarify the worth of Kubeflow because it pertains to MLOps.
Use Jupyter Notebooks in Kubeflow to evaluation the Kaggle OpenVaccine Problem Solution.
Define Kubeflow Pipeline utilizing Kale and Jupyter Notebooks hosted on Kubeflow Clusters.
Use Katib to carry out Hyperparameter Tuning with Kubeflow Pipelines.
Load Kubeflow Pipeline Snapshots in new Notebook Servers to revive earlier state.
Serve the best mannequin from a Jupyter Notebook.
Articulate how Machine Learning applied sciences come collectively to assist MLOps.
Requirements
Familiarity with common Data Science ideas
Description
The Kaggle OpenVaccine downside is a well-liked Data Science matter. In this course, you’ll discover the best way to resolve this downside with Kubeflow and Kale. In addition, youll find out how the work you’re doing is the muse for an efficient and self sustainable MLOps tradition and platform resolution which you could undertake at your enterprise.
This course is introduced as a sequence of palms on articles the place you’ll study Kaggle, Data Science, and MLOps whereas utilizing the Kubeflow platform with Kale to compile and run Kubeflow Pipelines. The total time dedication is about to . hours.
Specifically on this course, you’ll:
Learn about Kaggle.
Learn about Kubeflow.
Learn about MLOps.
Use Jupyter Notebooks in Kubeflow to evaluation the Kaggle OpenVaccine Problem Solution.
Use Kale to transform a Jupyter Notebook right into a Kubeflow Pipeline.
Use Katib to carry out Hyperparameter Tuning on the best OpenVaccine mannequin.
Load the Kubeflow Pipeline Snapshots in new Notebook Servers.
Serve the best OpenVaccine mannequin from a Jupyter Notebook.
Relate the actions on this course again to the core tenets of MLOps.
Requirements: We assume that you’ve familiarity with common Data Science ideas and have used a few of these philosophies in observe.
Instructor Led Option: If you would like to take the course dwell, this course is accessible on a month-to-month foundation with an teacher. If that is your choice, navigate and enroll on the Arrikto occasions web page.
Author s : Alexander Aidun, Ben Reutter
