Page 26 - Red Hat PR REPORT - JUNE 2024

Red Hat Enterprise Linux AI (RHEL AI) is a specialised extension of RHEL, optimised for
AI and machine learning applications. It provides a secure, stable, and scalable
platform for developing, deploying, and managing AI solutions, tailored for the unique

challenges and opportunities in emerging markets.
RHEL AI Features:

? Optimised Performance: Tailored for AI workloads, both on-premises and in the
cloud.
? Comprehensive AI Toolchain: Includes popular frameworks and libraries like

TensorFlow and PyTorch.
? Scalability: Supports large-scale AI deployments.

? Enhanced Security: Ensures data integrity and compliance with robust security
features.

? OpenShift Integration: Facilitates containerised deployment of AI models, enhancing
scalability and management.

OpenShift and Ansible: Amplifying AI's Effectiveness
Red Hat OpenShift and Red Hat Ansible Automation Platform are instrumental in
amplifying the effectiveness of AI within digital transformation initiatives. OpenShift

provides a robust, scalable, and flexible platform for deploying AI models, allowing
businesses to manage their AI workloads seamlessly across hybrid and multi-cloud

environments. Its containerised approach ensures consistent performance and
scalability, vital for handling large-scale AI operations.

Ansible Automation, on the other hand, streamlines and automates the deployment
and management of AI infrastructure. By reducing the complexity and manual effort

involved in managing AI environments, Ansible enables faster and more reliable AI
deployments, enhancing the overall efficiency of digital transformation efforts.
The Synergy Between Digital Transformation and AI

While digital transformation provides the strategic framework, AI offers the
technological capabilities to drive innovation and efficiency. In emerging markets, this

synergy is particularly potent, enabling businesses to leapfrog traditional barriers and
compete on a global stage.

Practical Applications:
? Predictive Maintenance: Anticipating equipment failures to minimise downtime.

? Customer Insights: Analysing data to personalise marketing and improve customer

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