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I did AWS training at the Amazon offices in Seattle for data science. I was blown away by the configuration... I have recompiled linux kernels and configured ip
by carabiner 4y ago
I did AWS training at the Amazon offices in Seattle for data science. I was blown away by the configuration... I have recompiled linux kernels and configured iptables as a teenager, and this was an entire galaxy of more complexity. It took us 6 hours to the point where some of us had a Jupyter Notebook running. Many people didn't make it though.
- goodpoint 4y agoArtificial complexity to justify lock-in and artificial salaries.
- wombatpm 4y agoOracle DBA’s would like to have a word with you in the back alley
- themoonisachees 4y agoThe microsoft model of "if sysadmins are spending much more time debugging windows, then they put windows first on their CV and gradually management forgets linux exists"
- Traubenfuchs 4y agoObviously! Just like devops as a whole. Why do we have this current mess instead of just pushing stuff to heroku?
- zeroxp 4y agoBecause heroku has limitations.
- rippercushions 4y agoWere you doing things the hard way on purpose? It's literally one click to spin up a Jupyter notebook on GCP, arguably less if you use Colab (instant access to shared instances), and I'm sure AWS has a similar service. https://cloud.google.com/vertex-ai-workbench https://cloud.google.com/vertex-ai-workbench
- carabiner 4y agoSo, if I remember right, the endgame was to have an automated ML model running as a service on an AWS instance with SageMaker. Part of that involved having a Jupyter setup installed. Don't remember much past that, though.
- crazygringo 4y agoWhy? That doesn't make any sense to me. What problems were you running into? Did you need to configure a bunch of super-non-standard stuff or something?