The building blocks and challenges for a distributed service are clear from the papers like Amazon Dynamo db, big table and several other Apache projects.
Interested in the challenges of machine learning infrastructure. What is a good place to start? What are the challenges? Any good papers or architectures for reference?
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Look at the papers/blog posts about ML platforms that have sprung up. Uber’s Michelangelo, Twitter’s DeepBird, Apple’s Alchemist, Google’s TFX.
After that you can take a look at available ML platforms/toolkits to understand what today’s problems are and how they’re being addressed - Amazon SageMaker, Kubeflow, TensorRT, Amazon Elastic Inference.
ML infrastructure is a very broad domain, but these are good reference points for today’s infrastructure challenges.
So if I want to grow and get expertise in ML infra should I be aware of the hardware aspects?
Big data infra has abstracted out most of it and seems to work well on commodity hardware. Never had the need to delve deeper for that case