Last week we hosted the second Kaggle’s House Prices workshop. In this blog post we’ll recap some highlights from the workshop. Ok, let’s dig in.
About the Kaggle Competition
With 79 explanatory variables describing nearly every aspect of residential homes located in Ames, Iowa, Kaggle’s House Prices competition challenges data scientists to predict the final price of each home.
What topics were covered in the workshop?
- Overview of Kubeflow
- Installing Kubeflow
- About the House Prices – Advanced Regression Techniques Competition
- Turning the House Prices competition into a Kubeflow Pipeline with the KFP SDK
- Turning the House Prices competition into a Kubeflow Pipeline with the Kale JupyterLab extension
- Comparing the Methods
What did I miss?
Here’s a short teaser from the 1 hour workshop where Jimmy walked folks through how to create a Kubeflow Pipeline for Kaggle’s House Prices competition using the Kale JupyterLab extension.
Want to see more? Here’s the YouTube playlist of all the demos featured in the workshop.
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Try More Use Cases for Yourself
If you’d like to walk through more Kubeflow use cases yourself, visit Arrikto Academy. Here’s some of the available courses:
- Kaggle’s Open Vaccine COVID-19 mRNA Vaccine Degradation Prediction
- Kaggle’s Titanic Disaster Survivor Prediction
- Kaggle’s Blue Book for Bulldozers Machine Learning Example
- Dog Breed Classification Example
- Distributed Training and Model Serving
- Kaggle’s Digit Recognizer Machine Learning Example
Missed the Sep 7 workshop?
If you were unable to join us last week, but would still like to attend a workshop in the future, register for one of these upcoming workshops.