You have sensitive data that cannot leave your data centers for compliance or other reasons.You need calculation capacity 24/7 (e.g.However, on-premises server farms form a valid alternative when your needs fulfill some of the following criteria: You’ll get access to the latest GPUs or, even TPUs that you wouldn’t be able to afford and maintain on your own. The cloud is by far the more scalable place to do machine learning. But very quickly you will run into the need more than your local CPU can provide you with.
Most machine learning experimentation starts from understanding your data on your laptop and doesn’t require that much computation power. scalability and security in ways that would be impossible for every other vendor on their own. They are also able to attract talent specialized in e.g. The cloud vendors are constantly innovating with solutions such as servers that only cost for the time they are used, as opposed to the time they are up and waiting for requests. It doesn’t make financial sense to build everything in-house when you can get it off the shelf for only the time you need it. The hyper cloud providers (AWS, Azure, GCP) are able to offer a smaller total cost of ownership while delivering superior features from scalability to security. But the fact remains, that by leveraging the cloud you can reap benefits that you couldn’t achieve with your on-premises server farm. It’s a running joke among developers that the cloud is just a word for somebody else’s computer. The cloud is just somebody else’s computer
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