Page 39 - Red Hat PR REPORT - MARCH 2024

3/19/24, 1:58 PM Navigating AI landscape: Fostering digital agility and resilience | MENAFN.COM
infrastructure impedes the workflow of data scientists and developers. Operationalising AI
projects further adds to the challenge, with slow, manual and siloed operations hindering the
swift execution of AI lifecycle. Acknowledging these challenges is crucial, and constant

vigilance is necessary to mitigate these setbacks to achieve success in AI/ML initiatives.

Companies can adopt strategic approaches to leverage AI/ML, such as enabling scale for AI-
enabled applications. This involves providing a consistent cloud application platform across
multiple private and public clouds, facilitating building, training and deployment of AI-enabled

applications. Moreover, collaborations with the ISV & SI community to offer AI tools, models
and services accelerates the development and deployment of AI solutions. Also, the adoption

of containers in AI workloads is gaining prominence, with 94 per cent of AI adopters
leveraging or planning the utilisation of containers in the coming year. This further
underscores the increasing significance of containerisation in developing and enhancing AI

workloads.

The hype surrounding AI/ML has sparked interest in new use cases and possibilities.
However, effective governance of data, applications and IT systems remains a key priority.
According to the 2023 Enterprise Cloud Index, organisations are leveraging multiple types of

IT infrastructure, indicating a shift toward diverse and hybrid environments. Building the
optimal AI-ready infrastructure has the potential to expedite AI/ML initiatives, but companies

should prioritise sustainability, cost management, security and other IT governance
compliance aspects.

The integration of AI into the open hybrid cloud is a significant step forward. To drive this
transformation, open-source artificial intelligence and machine learning (AI/ML) platforms like
Red Hat OpenShift AI offers a unified platform for data scientists and developers to design,

train, serve, monitor and manage the life cycle of AI/ML models and applications across
diverse environments. The platform aims at meeting the demands of foundation models and
ensures consistency in production deployment and monitoring capabilities. It further ensures

that customers can build and deploy intelligent applications seamlessly.

For instance, tech giants like Intel play a significant role in the AI ecosystem, showcasing their
investments in semiconductor manufacturing to strengthen supply chain resiliency. Intel’s
strategic investments in various fields aim to cultivate robust epicentres of technology and

thriving ecosystems for semiconductor manufacturing.

Furthermore, Intel’s AI Everywhere Portfolio extends from the data centre and cloud to the
client and edge. Its strategic investments in semiconductor manufacturing contribute to supply
chain resiliency, supporting diverse-owned suppliers and fostering innovation within the global

supply chain.

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