// Abstract

After years of failed POCs then all of a sudden one of our models is accepted and will be used in production. The next morning we are part of the main scrum stand-up meeting and a DevOps guy is assisting us. A strange feeling, unknown to us until then, starts growing on the AI team: we are useful!

Deploying models to production is challenging, but MLOps is more than that. MLOps is about making an AI team useful and iterative from the beginning. And it requires a role that takes care of the technical challenges that this implies, given the experimental nature of the ML field, while also serving the product and business needs. If your AI team does not include this role, maybe it's your time to step up and do it yourself! Today, we will chat with Ale about the transition from being a data scientist to a self-called MLOps engineer. And yes, you'll need to study computer science.

// Bio

Ale is born and raised in a mid-small town near Malaga in southern Spain. Ale did his bachelor's degree in robotics because it sounded cool and then he got into machine learning because it was even cooler.

Ale worked in two companies as an ML developer. Now he's on a temporary hiatus to study business and computer science and get a motivation boost.

--------------- ✌️Connect With Us ✌️ -------------

Join our slack community: https://go.mlops.community/slack

Follow us on Twitter: @mlopscommunity

Sign up for the next meetup: https://go.mlops.community/register

Catch all episodes, blogs, newsletter and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

Connect with Adam on LinkedIn: https://www.linkedin.com/in/aesroka/

Connect with Ale on LinkedIn: https://www.linkedin.com/in/alesolano/