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Consumer vs Server-Grade Hardware and why you can't do Business-Grade work with the formerIn any market or industry, quality work at scale requires purpose-built
high-performance tools.
In a previous article (The Eight Challenges You'll Face With On-Premise
Artificial Intelligence
[https://amalgam.ai/posts/the-eight-challenges-youll-face-with-on-premise-artificial-intelligence/]
), we promised to cover this topic more in-depth in a separate post. Well, we
like to keep our promises, so here we are.
It turns out that 3 of the Challenges we listed (Servers, Networking, and
Storage) ar
Distributed learning in an on-premise cluster - A Kaggle Reinforcement Learning case
Have you tried any distributed learning algorithms? If you are just starting out
in this area, I have my doubts, but if you have been on this path for a few
years, you might have faced one of those models. The incredible development of
the machine learning area in the last decade has not only brought a new state of
the art to several problems but has also taken processing optimization and
parallelization to another level. With increasingly larger models, any common
machine or even a single sup
Machine Learning Reproducibility: A Kaggle Competition Use-CaseEven though Reproducibility in Machine Learning is a theme that people hear
about now and then, we still see that people are practicing it only to a certain
degree. Even between Kaggle [https://www.kaggle.com/] competition winners, we
still see a lot of hard-to-reproduce code in Notebooks. Our goal here is to
outline some reproducibility elements and how we tackled them in a recent
competition.
First, what reproducibility stands for in Machine Learning? During a Machine
Learning project, we hav