Cloud Pak for Data Group

Videos v3.5: Watson Studio basics 

8 days ago

Watson Studio provides the data science features in a collaborative environment for data scientists, developers, and domain experts to explore, analyze, and model data.

Create a Watson Studio project from a zipped file and from a GitHub repository

Watch this video to see how to create a Watson Studio project in IBM Cloud Pak for Data from a zipped file and from a GitHub Repository.

Jupyter notebook basics in Watson Studio

Watch this video which covers the basics for working with Jupyter notebooks in Watson Studio.

Collaborate on projects

Watch this video to see how to add collaborators to a Watson Studio project in IBM Cloud Pak for Data so you can work with others on project assets.

Create a custom environment for Jupyter notebooks in Watson Studio

Watch this video to see how to create a custom runtime environment for use with a Jupyter notebook in Watson Studio.

Add platform connections

This video shows you how to create a connection to a data source at the platform level in IBM Cloud Pak for Data 3.5.

Add a connection and connected data to a project

Watch this video to see how to set up a connection to a data source and add connected data to a Watson Studio project in IBM Cloud Pak for Data.

Visualize and analyze precipitation data in a Jupyter notebook

Watch this video to see how to analyze annual precipitation data from the UNdata portal in a Jupyter notebook in a Watson Studio project.

Enable Git integration

If you enable Git integration in a Watson Studio project, then you can sync your project with a GitHub repository and allow collaborators to use the JupyterLab Integrated Development Environment (IDE). Watch this video to see how to enable Git integration in a Watson Studio project to use the JupyterLab IDE.

Create models with Watson Machine Learning

If you add Watson Machine Learning to Watson Studio, you can deploy and evaluate models. And Watson Machine Learning includes AutoAI, which gives data scientists superpowers by automating 80% of core data science processes like preparing data, selecting the best machine learning algorithm, and applying feature engineering. Watch this video to see how to use AutoAI to build a binary classification model, and then deploy and test that model.

Use the Data Refinery to shape raw data

Watson Studio and Watson Knowledge Catalog include the Data Refinery to saves you data preparation time by quickly transforming large amounts of raw data into consumable, quality information. Watch this video to see how to use the Data Refinery to shape raw data.


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