We love the words Machine Learning and Cloud. They almost go together when we talk about ML research, development and deployment. However, more often than not, your models may perform slow or your system faces downtimes and model degradation a lot. How do you ensure that your ML pipeline doesn’t face downtimes and become a bottleneck in the growth of your business?

At Dynopii, we work with real-time deep learning-based models for our products and services that run 24x7. One of the first challenges we solved was to keep our models running with 0 downtimes despite multiple updates to it every day. I’ll be talking about how to analyze your ML pipeline and how to design an ML Cloud solution architecture that keeps your critical business processes running smoothly through both planned and unplanned outages.

This session will be agnostic to cloud service providers!