Page 36 - Red Hat PR REPORT - MARCH 2024

A significant obstacle is the shortage of talent with key skills, making it challenging
to find and retain qualified professionals. Additionally, the lack of self-service access
to AI/ML tools and 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

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