Today, in most cases, cloud computing is the best option for developing and scaling Machine Learning projects. The reasons are clear and compelling:
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Data governance and security: cloud platforms offer advanced protection, helping reduce the risks of data breaches, leaks, or piracy.
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Access to new services: taking a cloud-native approach lets you instantly benefit from the latest features as providers roll them out. Essentially, you’re investing in the future—no need to reinvent the wheel, since continuous updates and innovations come built in.
That said, the cloud isn’t always the right answer. There are a few scenarios where sticking with on-premise infrastructure may be smarter:
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Legal or regulatory restrictions: if your organization handles highly sensitive data (such as medical records or government documents), laws and regulations may prevent you from moving it to the cloud.
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Legacy applications: if you’re running older applications that work fine but aren’t likely to be updated, migrating them may carry unnecessary risk.
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Specialized HPC: if you already have a costly, well-tuned High Performance Computing (HPC) cluster, it may be more cost-effective to keep it, while using the cloud only for specific parts of your workflow.
Bottom line: the cloud is often the winning choice for Machine Learning, but it’s not a one-size-fits-all solution. Evaluating your specific context is the key to making the right call.