Cloud Computing is the use of external computing resources for storage and computing tasks via the internet. As for the cloud itself, it's a collection of servers dedicated to this task and are made available, usually through some paid licensing, to anyone with an internet connection. Naturally, these servers are scalable, so you can always increase the storage and computing power you lease from a cloud provider. Although the cloud was initially used for storing data, e.g., for back-up purposes, it's used for various tasks, including data science work.
There are various kinds of machines used for cloud computing, depending on the tasks outsourced to them. For starters, there are the conventional (CPU) servers, used for storing and light computation. Most websites use this cloud computing option, and it's the cheapest alternative for cases where more specialized servers are utilized. However, for small-scale data science projects, especially those employing basic data models, these servers work well.
Additionally, there are the GPU servers that are more affordable for the computational resources they provide. Although GPUs are geared towards graphics-related work (e.g., the rendering of a video), they are well-suited for AI-based models. The latter make use of a lot of computations for the training phase of their function. As more and more data becomes available, this computational cost can only increase. So, having a scalable cloud computing solution that uses this type of server is the optimal strategy for deploying such a model.
Finally, there are also servers with large amounts of RAM, like the regular servers, but with plenty of extra RAM. Such servers are ideal for any use case where lots of data is involved, and it needs to be processed in large chunks. Many data science models fall into this application category since RAM is a valuable resource when large datasets are involved. Multimedia data, in particular, requires lots of memory to be processed at a reasonable speed, even for models that don't need any training.
Cloud computing has started to dominate the data science word lately. This phenomenon is partly due to the use of all-in-one frameworks, which take care of various tasks. These frameworks usually run on the cloud since they require many resources due to the models they build and train. As a result, unless there is a reason for data science work to be undertaken in-house, it is usually outsourced on the cloud. After all, most cloud computing providers ensure high-level encryption throughout the pipeline. The presence of cybersecurity mitigates the risk of data leaks or the compromise of personally identifiable information (PII) that often exists in datasets these days.
A great place that offers cloud computing options for data science is Hostkey. Apart from the conventional servers most hosting companies offer, this one provides GPU servers too. What's more, everything is at a very affordable price tag, making this an ideal solution for the medium- and the long-term. Check out the company's website for more information. Cheers!
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.