During this session, our speakers will present a set of open-source tools widely adopted and compatible with most environments including data centers, clouds, and mainframes.
Cloud services or even on z/OS mainframes such as about Python as programming language, Jupyter notebook as interface, DataFrames as data layer.
Machine Learning (ML) is a reality on mainframe platforms; however, we are not sure where the best place is to process the historical data, train the data, create the models, and finally in realtime how to apply the model to the production data in a satisfactory way.
This study presents a set of open tools that are widely adopted and compatible with the mostly ML platforms including your local station, cloud services, or even on z/OS mainframes. We will talk about Python as programming language, Jupyter notebook as interfaces, DataFrames as data abstraction layer, and MLlib and Apache Spark as execution engines.
Attendees will have a brief introduction to key components in ML, a good starting point to explore your system metrics, and learn some new fancy words.
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