Use the power of data science to quickly and effectively solve machine learning problems by using Apache SystemML. This code pattern uses Jupyter Notebooks running on IBM Watson Studio to perform a machine learning exercise.
In this pattern, we’ll use Apache SystemML running on IBM Watson Studio to perform a machine learning exercise. Watson Studio is an interactive, collaborative, cloud-based environment, where data scientists, developers, and others interested in data science can use tools (such as RStudio, Jupyter Notebooks, Spark) to collaborate, share, and gather insight from their data. Apache SystemML is a flexible machine learning platform that is optimized to scale with large data sets.
When you have completed this pattern, you’ll learn how to:
- Use Jupyter Notebooks to load, visualize, and analyze data
- Run Notebooks in IBM Watson Studio
- Leverage Apache SystemML as a machine learning library
The audiences for this code pattern are application developers and other stakeholders, who want to use the power of data science to quickly and effectively solve machine learning problems by using Apache SystemML. Although Apache SystemML provides various out-of-the box algorithms to experiment with, this pattern provides a linear regression example to demonstrate the ease and power of Apache SystemML. Additionally, users can develop their own algorithms by using Apache SystemML’s Declarative Machine Language (DML), which has R or Python like syntax, or customize any algorithm provided in the package. For more information about additional functionality support, documentation, and the roadmap, see Apache SystemML.