2 Securely Leverage Open-Source Software with Python AI Toolkit for IBM z/OS
Did you know?
The zIIP eligibility list was extended to include Python-based applications, so applications that
are built by using the Python AI Toolkit for IBM z/OS benefit by scaling smoothly and
remaining cost-effective.
Business value
Python AI Toolkit for IBM z/OS is a part in providing a secure foundation for your AI software
stack. With this foundation, you can drive real-time AI insights within mission-critical
workloads on IBM zSystems®. Some example applications of AI solutions include fraud
detection, anti-money laundering, and image recognition.
Minimizing security exposures and vulnerabilities that might compromise the safety of the
operational environment is a priority for enterprises. To mitigate these risks, Python AI Toolkit
for IBM z/OS provides the following capabilities:
OSS currency that significantly reduces the impact of potential vulnerabilities.
All the OSS that is hosted in the library is scanned and vetted for security vulnerabilities by
using supply chain security.
OSS is delivered through an IBM-owned repository, which means that IBM is the only
content contributor and provider. This level of access control helps ensure the reputability
of the OSS.
Because OSS is an essential component of the AI ecosystem, and Python is a popular and
modern programming language in the AI industry, combining the two is foundational to the
development of AI solutions. With the Python AI Toolkit for IBM z/OS, you can develop Python
applications within the IBM zSystems environment, which raise the awareness, interest, and
feasibility of using modern programming languages to develop and deploy AI applications on
IBM zSystems. Your enterprise can appeal to a wider pool of new talent and potentially
reduce the skill gap when recruiting and retaining developers.
Solution overview
The Python AI Toolkit for IBM z/OS can be thought of as a runtime library that contains many
of the most widely used Python packages for AI and ML workloads, including the following
packages:
NumPy, SciPy, and Scikit-learn for numerical computation
Jupyter, JupyterHub, and Pandas for application development
Matplotlib and Cairo for graphics support
XGBoost and Apache Toree (for interacting with Apache Spark from Jupyter) for ML
frameworks
Developers can interact with these packages directly by writing their own applications or use
them indirectly through products like IBM Watson Machine Learning for z/OS.
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