Since real-world... «Distributed Machine Learning Patterns»
bru_sia25 октября 2025Since real-world distributed machine learning workflows can be extremely complex, as seen in chapter 5, a huge amount of operational work is involved to help maintain and manage the various components of the systems, such as improvements to system efficiency, observability, monitoring, deployment, etc. These operational work efforts usually require a lot of communication and collaboration between the DevOps and data science teams. For instance, the DevOps team may not have enough domain knowledge in machine learning algorithms used by the data science team to debug any encountered problems or optimize the underlying infrastructure to accelerate the machine learning workflows. For a data science team, the type of computational workload varies, depending on the team structure and the way team members collaborate. As a result, there's no universal way for the DevOps team to handle the requests of different workloads from the data science team.
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