Model deployment strategies

MLOps Deployment and Life Cycling

Nemanja Radojkovic

Senior Machine Learning Engineer

Deployment successful!

  • ML app is running
  • API handles 1000s of requests/hour
  • One month later:
    • New, better features found
    • Collected new batch of training data
    • Ran the model build pipeline
    • New model package created
MLOps Deployment and Life Cycling

swapping

MLOps Deployment and Life Cycling

offline prediction

MLOps Deployment and Life Cycling

change window

MLOps Deployment and Life Cycling

real time downtime

MLOps Deployment and Life Cycling

expensive downtime

MLOps Deployment and Life Cycling

blue green setup

MLOps Deployment and Life Cycling

click of a button

MLOps Deployment and Life Cycling

blue green term

MLOps Deployment and Life Cycling

advantage disadvantage

MLOps Deployment and Life Cycling

rollback

MLOps Deployment and Life Cycling

canary deployment

MLOps Deployment and Life Cycling

request splitting

MLOps Deployment and Life Cycling

step two

MLOps Deployment and Life Cycling

final result

MLOps Deployment and Life Cycling

MLOps Deployment and Life Cycling

validation

MLOps Deployment and Life Cycling

disadvantage

MLOps Deployment and Life Cycling

risk mitigation

MLOps Deployment and Life Cycling

Let's practice!

MLOps Deployment and Life Cycling

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