Technology advancements are happening in nearly every industry, and this is largely because of the advancements in artificial intelligence and machine learning. In order to keep up with the increasing demand for rapid insights from data, machine learning teams are looking for ways to manage and automate their ML pipelines in order to speed up model deployment. While MLOps streamlines the ML development lifecycle by delivering automation, CI/CD creates a truly end-to-end pipeline that closes the feedback loop at every step of the way, and maintains high performing ML models.

In this webinar you will learn techniques for implementing and automating continuous integration (CI), continuous delivery (CD), and continuous training (CT) for machine learning systems. You will get leading strategies from a cnvrg.io AI expert on how to monitor and retrain models in production, and ensure your models run smoothly in a production environment. 

What you’ll learn in this webinar: 

• What is MLOps, and how does it differ from DevOps?
• How to apply CI/CD principles to automate your ML pipelines
• Considerations for deploying machine learning into production
• How to help more than just data professionals get machine learning into production
• How to go from manual MLOps to automated ML pipelines