Life-cycling stages

MLOps Deployment and Life Cycling

Nemanja Radojkovic

Senior Machine Learning Engineer

Machine Learning life cycles

 

1. ML project life cycle
2. ML application life cycle
3. ML model life cycle

A model is...

NOT something abstract and/or theoretical

A model IS

• a concrete, trained model
• ready to be put in use

Deployment

 

• Model object + deployment resources = model deployment package
• Putting our model in use = deployment

 

Model monitoring

 

• Is the model server running?
• Are the model inputs and outputs as expected?
• Also known as post-deployment monitoring

 

Decommissioning

 

 

Possible reasons

• Better model discovered
• More informative features created
• Modeled process changed, invalidating the existing model

 

=> Out with the old, in with the new

Model archiving

• Important in regulated industries.

  Why did your credit-risk model green-light this loan back in 2020?

 

• Reproducibility: ability to recreate, re-load, and use an old version of a model
  • Not a trivial requirement

 

Let's practice!

MLOps Deployment and Life Cycling