In this section you will learn about model training on the Data Science cloud service using a variety of popular frameworks. This section
covers the popular
sklearn framework, along with gradient boosted tree estimators like LightGBM and XGBoost, and
deep learning packages likes TensorFlow and PyTorch.
The section covers how to serialize models and make use of the OCI Model Catalog to store model artifacts and meta data all using ADS to prepare the upload.
In the distributed training section you will see examples of how to work with Dask, Horovod, TensorFlow and PyTorch to do multinode training.
TensorBoard provides the visualization and the tooling that is needed to watch and record model training progress throughout the tuning stages.