How to choose a cloud machine learning platform

12 capabilities every cloud machine learning platform should provide to support the complete machine learning lifecycle

How to choose a cloud machine learning platform
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In order to create effective machine learning and deep learning models, you need copious amounts of data, a way to clean the data and perform feature engineering on it, and a way to train models on your data in a reasonable amount of time. Then you need a way to deploy your models, monitor them for drift over time, and retrain them as needed.

You can do all of that on-premises if you have invested in compute resources and accelerators such as GPUs, but you may find that if your resources are adequate, they are also idle much of the time. On the other hand, it can sometimes be more cost-effective to run the entire pipeline in the cloud, using large amounts of compute resources and accelerators as needed, and then releasing them.

The major cloud providers — and a number of minor clouds too — have put significant effort into building out their machine learning platforms to support the complete machine learning lifecycle, from planning a project to maintaining a model in production. How do you determine which of these clouds will meet your needs? Here are 12 capabilities every end-to-end machine learning platform should provide. 

Be close to your data

If you have the large amounts of data needed to build precise models, you don’t want to ship it halfway around the world. The issue here isn’t distance, however, it’s time: Data transmission speed is ultimately limited by the speed of light, even on a perfect network with infinite bandwidth. Long distances mean latency. 

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