Can you outline the stages of implementing a machine learning model into a production environment?

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Anonymous · Accepted Answer

In dev, the model training of different datasets must be tracked. This means:
* Tracking the dataset used for training, validation and testing
* Tracking ML algorithm and its parameters
* Train the algorithms + different parameters on the same training dataset,
validate and test on the same validation & testing dataset.
* Compare the output of the algorithms on the test dataset.
* all of the above steps must be tracked (using MLFlow)

Then when the final algorithm is picked, the code + model must be pushed to git
repo. 
The MLOps framwrok insures the code and model goes to standard workflow of
dev/staging/prod. In Staging, a code review will be done (by someone else and
maybe on different dataset). When all ticked, it will go to Prod and then
realease

Can you outline the stages of implementing a machine learning model into a production environment?

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