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
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."
"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."