AS GENERATIVE AI USE CASES MOVE FROM DABBLING PROOF OF CONCEPTS TOWARDS MEANINGFUL EXERCISES , DESCRIBE THE VARIOUS COMPUTE AND STORAGE HYBRID CLOUD PLATFORMS THAT ARE AVAILABLE TO SCALE THESE USE CASES ?
EDITOR ’ S QUESTION
AS GENERATIVE AI USE CASES MOVE FROM DABBLING PROOF OF CONCEPTS TOWARDS MEANINGFUL EXERCISES , DESCRIBE THE VARIOUS COMPUTE AND STORAGE HYBRID CLOUD PLATFORMS THAT ARE AVAILABLE TO SCALE THESE USE CASES ?
Generative AI is not the first technology to put decision makers in a bind , caught between risk management on one side and a runaway adoption trend on the other . Cloud computing was met with the same mix of enthusiasm and trepidation . Generative AI , like cloud computing before it , has an appeal that spans industries and scales .
The demand for robust computing and storage platforms has become essential as Generative AI transitions from experimental projects to business-critical applications . Hybrid cloud solutions are emerging as the backbone for scaling these applications . Executives from Cloudera , Pure Storage , NetApp , ServiceNow , provide more insights to the question .
KARIM AZAR , REGIONAL VICE PRESIDENT OF MIDDLE EAST
AND TÜRKIYE , CLOUDERA
The demand for robust computing and storage platforms has become essential as Generative AI transitions from experimental projects to business-critical applications . Hybrid cloud solutions are emerging as the backbone for scaling these applications , providing organisations with scalability , flexibility , and cost-efficiency .
These platforms enable optimised performance , streamlined operations , and sustainable cost management by seamlessly integrating on-premises and cloud environments Hybrid data platforms offer a centralised framework for managing data , applications , and users across diverse environments , including on-premises and cloud providers like AWS , Azure , and Google Cloud Platform , GCP . These platforms allow organisations to deploy AI models flexibly , regardless of data location , and ensure compliance for industries with stringent regulatory requirements .
Hybrid platforms integrate GPU-accelerated compute resources , such as NVIDIA GPUs , to handle demanding AI workloads . Distributed storage solutions , including object stores and high-performance file systems , manage the vast data volumes generated by AI workflows . This combination ensures efficient data access and management without compromising performance . Moreover , hybrid platforms help organisations optimise costs by leveraging on-premises resources for predictable workloads while using the cloud for elasticity during peak demand . This balance ensures cost-effective scalability and consistent performance for large-scale AI applications .
Cloud providers like AWS , Azure , and GCP offer native AI and machine learning services that integrate seamlessly with hybrid deployments . These services allow organisations to conduct resource-intensive tasks like data processing and model training in the cloud , while inference and operational activities can remain on-premises or closer to the edge , reducing latency and enhancing efficiency .
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