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MLRun Functions DEMO: Python Jupyter (Open-Source Data Science Orchestration + Experiment Tracking)

MLRun is a generic and convenient mechanism for #data scientists and software developers to build, run, and monitor #machinelearning (ML) tasks and pipelines on a scalable cluster while automatically tracking executed code, metadata, inputs, and outputs. On-Premise or Barebone/Metal - including Edge AI / Analytics Customers include NetApp, Quadient, Payoneer (and many more).

How to handle errors on elastic.io

You can see if any of your flows have errors as soon as you log in to your dashboard. It shows you the number of records processed in total – in green – and the number of records with errors – in red. You can also see the same information if you click on the corresponding flow from the dashboard. In my case, you can see I have three records processed and all three returned errors.