Are your MLOps foundations ready to scale in 2023?

It's a new year and you might want to make sure your production ML operation is ready to scale and deliver  significant business impact.

There are certain must-have features that can help take your system to the next level and accomplish production goals.
ML engineering teams should look for MLOps capabilities that enable them to design, build, and deploy their models faster and with greater simplicity and ease.

From strategic ways to automate data pipelines and optimize resource utilization, to insights into model performance metrics and and versioning.

Join Yuval Fernbach, Co-founder and CTO @Qwak for a session that will help you get ahead of the curve this coming year by investigating which capabilities your production MLOps needs.

Yuval Fernbach

Yuval Fernbach

CTO & Co-founder

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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."

Shaked Zychlinksi Head of Recommendations Research

"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

"Qwak streamlines our machine learning development all the way from prototype to production, freeing us from infrastructure concerns and maximizing our focus on business value delivery."

Edward Zhou,
Software Engineer