Why is it that 80% of enterprises fail to scale AI? Data scientists face operational, collaborative and infrastructure complexities at each step of the ML lifecycle. MLOps practices have the ability to solve many ML operational concerns such as project deployment, testing, serving and monitoring. In this webinar, Yochay Ettun, CEO and Co-founder of cnvrg.io will discuss the ways that MLOps solutions empower data scientists to successfully operationalize ML by applying DevOps principles to the ML lifecycle.

We’ll answer the following key questions: 

• What is MLOps, and what does it solve? 
• What are the key challenges of building successful ML outcomes?
• What does a production ML workflow look like?
• What are the architectural complexities and considerations for enterprise AI?
• How does MLOps solve data science challenges? 
• How does MLOps help data scientists explore, train and deploy AI?
• How MLOps accelerates research and rapid prototyping
• How to get started with MLOps