Machine Learning Operations, or MLOps, is a set of practices that aims to deploy and maintain machine learning models reliably and efficiently.

Similar to the DevOps approach, MLOps seeks to increase automation and improve the quality of production models while focusing on business requirements.

In the first part of this three-episode series on the ‘Transformational Serverless Acceleration for Analytics’, Analyst Christina Stathopoulos speaks to Richard Heyns, Founder and CEO of Brytlyt to cover:

• Why do two-thirds of models built with the intention to deploy never make it to deployment
• Databases and the critical role they play
• Scaling up analytics and ML