Source: medium.com
Azure MLOps: Doing DevOps one betterCategory: Data
MLOps is everything that DevOps is, plus the part where it takes care of your ML model training along with dataset and model management. The model worked as required and expected, and it was time to deploy the application which made use of the model.
Set up a pipeline which:- Prepared my data for model training- Saved the prepared data as a versioned dataset- Trained the model- Maintained the model’s version
In this step, we have also used the previously defined pipeline data, “training_step_output” which is basically a path, to store the output and share it with a different pipeline step. register.py is executed on a different compute target and the pipeline data, “training_step_output” in which the training output was stored is used.register.py would look something like this: In the end, we establish the sequence of the previously defined steps for the pipeline, validate it (check for circular dependencies of pipeline data etc).
Set up a pipeline which:- Prepared my data for model training- Saved the prepared data as a versioned dataset- Trained the model- Maintained the model’s version
In this step, we have also used the previously defined pipeline data, “training_step_output” which is basically a path, to store the output and share it with a different pipeline step. register.py is executed on a different compute target and the pipeline data, “training_step_output” in which the training output was stored is used.register.py would look something like this: In the end, we establish the sequence of the previously defined steps for the pipeline, validate it (check for circular dependencies of pipeline data etc).
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