Training an ML Model
Last updated
Last updated
To train an ML model, follow these steps:
Log in to the Aizen Jupyter console. See .
Set the current working project.
Configure a training experiment by running the configure training
command:
In the notebook, you will be guided through a template form with boxes and drop-down lists that you can complete to define the experiment. At a minimum, you must choose either Machine Learning
or Deep Learning
, and you must specify the input features and the output (or label) features from the dataset. Additionally, you may specify the feature types and specify several options in the advanced settings.
Execute the training experiment using the start training
command to schedule a job. Optionally, you may configure resources for the job by running the configure resource
command. If you do not configure resources, default resource settings will be applied.
While the job is running, you may check the job status and check the training progress on TensorBoard or MLflow by using the URL shown in the status training
command or by listing the TensorBoard or MLflow URL for the training run.
Wait for the job to complete, and then check your training results:
List the trained models and display training results:
You may compare the results of various training runs of a given ML model using the TensorBoard or MLflow URL for the ML model: