Improvements to the Notebook Session Environment and Library Upgrades

Notebook Session Environment

The notebook session environment includes these improvements:

New Launcher buttons for both the notebook examples and the getting started notebook.The names of these notebook examples are shortened to make it easier for you to find the one you are looking for.
A new library, kafka-python, is installed. The kafka-python library is a Python client for the Apache Kafka distributed stream processing system and allows data scientists to easily connect to the Oracle Streaming Kafka-compatible API.
The Ctrl + f (find) bug in JupyterLab was fixed.

The library upgrades are:

Oracle Accelerated Data Science (ADS) SDK

Numerous bug fixes including :

Support for Oracle Cloud Infrastructure Data Flow service applications, which runs outside of notebook sessions. compartment. Support for specific object storage logs and scripts buckets at the application and run level.
Customizable scoring function to score AutoML models. The class_weighting.ipynb notebook example demonstrates this use case.
ADS detects small shapes and gives warnings for AutoML execution.
Removal of triggers in the Functions, func.yaml, file.
DatasetFactory.open incorrectly yielding a classification dataset for a continuous target was fixed.
LabelEncoder producing the wrong results for category and object columns was fixed.
An untrusted notebook issue when running model explanations visualizations was fixed.
A warning about adaptive sampling requiring at least 1000 datapoints was added.
New version uptakes for both MLX (version 1.0.7) and AutoML (version 0.4.2).

Notebooks

New notebook example showcasing the use of the Vault to securily pull credentials and secrets like passwords, keys, etc. in a notebook session.
New notebook example showing a step-by-step approach to connect to Oracle Streaming using the kafka-python client library.
We also revised, modified, and improved the content of most of the existing notebooks.