MLOps 2.0

From research centric to production first
Yuval Fernbach
Yuval Fernbach
Co-founder & CTO at Qwak
at
at
at

MLOps needs no introduction.

MLOps 2.0 is the much awaited next phase to make ML really happen.

In a nutshell it's a comprehensive approach to the ML pipeline that makes sure each stage of the model pipeline is ready for and in production.

If your models are doing great in experimentation but you are still trying to put all the production pieces together, this session might help you understand what's going wrong and how to fix it.

By working according to this methodology data scientists can iterate rapidly which is at the core of a successful ML project.

Join Yuval Fernbach, Co-founder and CTO at Qwak to learn how to:

- Build a feature pipeline that can run in production

- Maintain a centralized production focused model registry

- Monitor, track and react in your production ML environment

MLOps needs no introduction.

MLOps 2.0 is the much awaited next phase to make ML really happen.

In a nutshell it's a comprehensive approach to the ML pipeline that makes sure each stage of the model pipeline is ready for and in production.

If your models are doing great in experimentation but you are still trying to put all the production pieces together, this session might help you understand what's going wrong and how to fix it.

By working according to this methodology data scientists can iterate rapidly which is at the core of a successful ML project.

Join Yuval Fernbach, Co-founder and CTO at Qwak to learn how to:

- Build a feature pipeline that can run in production

- Maintain a centralized production focused model registry

- Monitor, track and react in your production ML environment

Qwak optimizes ML Model Production

“We ditched our in-house ML platform for Qwak. I wish we had found them sooner.”
Upside
“Qwak streamlines ML development from prototype to production, freeing us from infrastructure concerns and maximizing our focus on business value.”
Notion
“People ask me how I managed to deploy so many models while onboarding a new team within a year. My answer is: Qwak.”
OpenWeb
“With Qwak, our ML team efficiently manages and deploys various models, both batch and real-time. The addition of an observability and Vector DB layer has been a game-changer, allowing us to confidently bring 10 models into production. Qwak's robust and streamlined approach has significantly enhanced our operational efficiency.”
Happening (Superbet)