Building vs. Buying Your ML Platform

What to consider before building or buying an ML platform
Ran Romano
Ran Romano
Co-founder & CPO at Qwak
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As organizations embrace the potential of data, they face a challenge of operationalizing machine learning and data science delivery processes.Building the infrastructure to support machine learning efforts requires experience, and resources.

Join Qwak's VP of R&D, Ran Romano to hear from his experiences about:

  • What should your ML engineering platform be able to deliver?
  • The points to consider whether you build or buy

As organizations embrace the potential of data, they face a challenge of operationalizing machine learning and data science delivery processes.Building the infrastructure to support machine learning efforts requires experience, and resources.

Join Qwak's VP of R&D, Ran Romano to hear from his experiences about:

  • What should your ML engineering platform be able to deliver?
  • The points to consider whether you build or buy

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)