Model Training Options
Last updated
Last updated
Modification of the model training option setting is not possible after Training begins.
Prioritize training to start before other projects in the training queue.
Sets the GPU instance performance to use for AI training.
Set the training form according to the AI function to be targeted. Depends on the type of data selected.
Structured Data Automatic Classification
Image Classification
Structured Data Category Classification
Object Detection
Structured Data Regression
Natural Language Processing(NLP)
Recommender System(Matrix)
Depending on your purpose, you can choose the right way to train.
Manual Setting Machine learning algorithms and hyperparameters can be manually set to training, and it is suitable for expert-level handling.
Generate Code Generate a training code that allows you to start training right away by pasting it into the Jupyter.
Higher Accuracy(AutoML) It is a method of training a slower but more accurate model, and it is useful for deriving the final model.
Faster Training Speed(AutoML) It may be difficult to obtain a high-accuracy model, but it is a way to train more quickly to identify the model, which is useful for POC production.
Based on the input data, select the value corresponding to the result data to be output by the AI model that has been trained.
Select the algorithm library to use for training.
You can adjust the hyperparameter values directly. You can enter multiple values by pressing Enter
in each input window.
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