The pitfalls to expect when building vs buying your ML platform

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
Ran Romano

Ran Romano

VP Engineering

Watch Now

Qwak optimizes ML Model Production for
ML driven organizations.

Here is what our customers have to say about us:

"From the get go, it was clear that Qwak understand our needs and requirements. The simplicity of the implementation was impressive.
Automatic deployment and continuous training were crucial to allow us to scale. Qwak gave us a type of "Jenkins" for machine learning."

quote
Shaked Zychlinksi Head of Recommendations Research
jll

"Using Qwak allowed us to focus on creating a business impact rather than spending valuable time on our infrastructure setup.

At JLL our development is very time sensitive. As a result of implementing Qwak, we improved our execution time by 4.5X."
‍

or hiltch
Orr Hiltch, Vice President of Engineering

"With Qwak we were able to improve our ML delivery dramatically.

Qwak has allowed us to work to the highest engineering standards from day one and to invest the majority of our efforts in our business challenges and not into plumbing."
‍

Elad Silvas Data Science Manager