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.
CTO & Co-founder
"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."
"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."
"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."