MLOps Conference

MLOps Conference

On DataOps, productionizing ML models, and running experiments at scale.



Neha Gupta


Automatic rollbacks for MLOps deployments in Kubernetes

Submitted Jun 18, 2021

While there are different tooling to automate deployments of ML models most of them require manually written rules for verifying deployments in production.

Would love to show community how to enable automatic rollbacks in their model deployment pipelines without needing any log regexes, monitoring rules to verify the health of the deployments. This is enabled via HybridK8s Droid, to learn more -

I will also talk more about how to embrace SRE mindset while managing production models and ensuring maximum uptime without needing too many SRE/DevOps tooling.

Key Takeaway :

  • How to reduce production downtime/MTTR using progressive deployments?
  • How to reduce lead time of productionising ML applications?
  • How to reduce efforts required to implement/maintain deployment pipelines?

Audience :
Anyone concerned about the stability of production models - Data/DataOps Engineers, Engineering Management


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Scribble Data builds feature stores for data science teams that are serious about putting models (ML, or even sub-ML) into production. The ability to systematically transform data is the single biggest determinant of how well these models do. Scribble Data streamlines the feature engineering proces… more


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