Implementing AI on IBM Z and LinuxONE is about leveraging their inherent power and security. The main best practice is Data Proximity-you want to run your AI models right where your sensitive transactional data already resides to minimize latency and risk. The good news is these platforms fully support the standard open-source AI ecosystem, which includes Python environments and major frameworks like TensorFlow and PyTorch, all optimized for the $s390x$ architecture. For development, you'll rely on tools like Anaconda and Jupyter Notebooks. When it comes time to deploy and manage these models, you'll want to containerize them using Docker or Podman, ideally orchestrated with OpenShift, and manage the lifecycle (MLOps) using platforms like IBM Watson Machine Learning or MLflow. Simply put, you get the performance and security of the mainframe with the flexibility of open-source AI tools, making it perfect for high-speed, secure tasks like real-time fraud detection.
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john wick
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Original Message:
Sent: Thu October 30, 2025 03:05 PM
From: Martin Luther
Subject: Seeking guidance on AI implementation on IBM Z & LinuxONE
Subject: Seeking guidance on AI implementation on IBM Z & LinuxONE
Hello IBM Community members,
I'm reaching out to seek guidance and recommendations from this community regarding the implementation of AI solutions on IBM Z & LinuxONE.
I'm particularly interested in learning about best practices, available tools, and any experiences you may have had in this area.
Thank you in advance for your insights and support.