The Journey from Data Scientist to MLOps Engineer // Ale Solano // MLOps Coffee Sessions #80

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MLOps Coffee Sessions #80 with Ale Solano, The Journey from Data Scientist to MLOps Engineer.

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// 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 was born and raised in a mid-sized 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.

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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/

Timestamps:

[00:00] Brief Introduction to Adam Sroka

[00:47] Takeaways

[04:27] Support the community!

[05:37] Introduction to Ale Solano

[06:52] How Ale Solano got into MLOps

[09:16] Getting aboard the ML train

[10:51] Robotics to Computer Science

[14:54] Early MLOps headaches

[16:54] SPRINT by Jake Knapp

[17:58] Starting to implement MLOps

[19:44] Major adjustment

[21:34] Biggest wins

[22:49] Importance of CICD

[24:50] Major Stakeholders of Ale's ML team

[26:55] The dream the community must have

[30:33] Recognizing the foundational pieces and the evolution

[33:13] What is Ale excited about, and what is missing

[34:36] Different fields of expertise

[36:49] Ale's take on "80% of the models don't make it to production"

[39:15] Wrap up