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When AWS, Azure, or GCP Becomes the Competition

gkogan.co

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Re: When AWS, Azure, or GCP Becomes the Competition

#91
post #65

Earlier quoted context omitted.

How do you train your models? With Databricks (which is just a nice notebook UI for Apache Spark) I don't have to spend time setting up VMs and libraries for the distributed training.

Your first sentence seems unrelated to your second sentence, but it seems like you think they are related? Any platform that advertises something like you don’t have to spend time defining your training environment, whether it’s Databricks or an out of the box deep learning VM on GCP, is a liability waiting to happen. You always need to define your own training environment, especially because you’ll almost always nee…

Interesting, sounds like you have a very specific usecase. I'm mostly dealing with huge datasets and Spark is a lifesaver.

Re: When AWS, Azure, or GCP Becomes the Competition

#92
post #91

Earlier quoted context omitted.

Your first sentence seems unrelated to your second sentence, but it seems like you think they are related? Any platform that advertises something like you don’t have to spend time defining your training environment, whether it’s Databricks or an out of the box deep learning VM on GCP, is a liability waiting to happen. You always need to define your own training environment, especially because you’ll almost always nee…

Interesting, sounds like you have a very specific usecase. I'm mostly dealing with huge datasets and Spark is a lifesaver.

My use case is just general computing on huge datasets for ML and statistics.

Spark only serves a few special use cases that usually don’t justify its cost, similar to map reduce on Hadoop.

Databricks though (distinct from Spark) serves no use cases.

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