Machine Learning & AI

Provides various usage examples for building and utilizing a Machine Learning & AI environment.
Detailed service-specific documentation for KakaoCloud used in the tutorial can be found in the service category.

Tutorials

📄️ Setting up Jupyter Notebook environment using Kubeflow

Set up an MLOps environment on Kubernetes using KakaoCloud's Kubeflow service.

📄️ Implementing a Predictive Model with Kubeflow Notebook

Practice implementing a taxi fare prediction model using a dataset in Jupyter Notebook.

📄️ Training predictive model using Kubeflow pipelines

Learn how to create experiments and runs, and train a predictive model using pipeline exercises.

📄️ Managing machine learning experiments with Kubeflow tensorBoard

Use TensorBoard in the Kubeflow environment to manage and visualize machine learning experiment logs.

📄️ Hyperparameter tuning with Kubeflow

Create an AutoML experiment through a hyperparameter tuning exercise.

📄️ Implementing a Parallel Training Model with Kubeflow MIG Instance

Utilize multiple GPU resources with MIG settings to implement prediction models in notebooks/pipelines.

📄️ Creating Kubeflow model serving API

Learn how to create and serve a model trained in a pipeline as an API using a sample dataset.