Summary
In this chapter we learned what are Kaggle Notebooks, what types we can use, and with what programming languages; we learned how to create, run, and update Notebooks. We visited then the most common features for using notebooks: how to connect them to data sources, models, setting accelerators, environment, internet access. Next, we reviewed less frequent used features: use of utility scripts, secrets, connection to Google Cloud to use Google Cloud Storage, BigQuery or AutoML services and also how to use features from Kaggle Notebooks interface to automate datasets update. Finally, we introduced use of Kaggle API to further extend your usage of Notebooks, allowing you to build external data and ML pipelines that integrates with your Kaggle environment.
