Google Cloud ML Engine
Projects based on machine learning require a lot of resources. From storage to computational power, training models sometimes require resources that can not be found on a simple computer. Physical limitations in terms of storage space have been reduced in recent years. Also, computing power has grown massively; many operations can be performed just on laptops. However, despite all this very rapid evolution, scientific analysts often find themselves in difficulty when it comes to addressing big data problems due to the necessary calculation and space resources. Cloud computing services have been developed to meet these requests for unlimited space and computing power.
The term cloud computing indicates a paradigm for ...
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