This post contains some nice tips if you are planning some machine learning in the cloud.
In the fourth post of the series, I compared prediction functionality and performance between each of the services. We saw that while some services may make more accurate predictions than others, the runners-up often follow closely behind. That’s good news for you because it means you are free to choose among the services without being too concerned that you picked a dud when it comes to making accurate predictions. This post will cover some other miscellaneous topics that may help you choose which service will best meet your needs.
I previously hinted that I ran into stability issues with more than one service. Which ones gave me grief? Well, actually, all of the cloud-based services had problems. I was unable to rely on any of them to take my data, create a model, and then make predictions without occasionally failing. To collect the results, I often had to run experiments multiple times (without any…
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