MLOps Coffee Sessions #113 with Leanne Fitzpatrick, Director of Data Science of Financial Times, Building Better Data Teams co-hosted by Mihail Eric.

// Abstract
We spent a lot of time talking about data tooling but we maybe spent not as much time talking about data organizations and efficiently running and organizing data teams.   

What about starting with limitations instead of aspirations? Right constraints instead of the north star? In this session, let's learn more about a realistic take on the state of data organizations of today.

// Bio
Leanne is Director of Data Science at the Financial Times and is a passionate data leader with experience building and developing empowered data science and analytics teams in a variety of businesses. Leanne is in her element when developing and implementing strategic, technical, and cultural solutions to getting machine learning and data science into the operational ecosystem.

Leanne is an active part of the data and technology community, sharing innovation and insights to encourage best practices, from Manchester, UK to Austin, TX, and is an Advisory Panel Board Member. Outside of all things data you can ask Leanne about her golf swing (it’s not good - yet), her passion for American Football (specifically the Cincinnati Bengals), her latest sewing project, and her love for good music, food, and whisky.

// MLOps Jobs board  
https://mlops.pallet.xyz/jobs

MLOps Swag/Merch
https://mlops-community.myshopify.com/

// Related
Links Children of Time book by Adrian Tchaikovsky:  
https://www.amazon.com/Children-Time-Adrian-Tchaikovsky/dp/0316452505

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Mihail on LinkedIn: https://www.linkedin.com/in/mihaileric/
Connect with Leanne on LinkedIn: https://www.linkedin.com/in/leanne-kim-fitzpatrick-29204341/

Timestamps:
[00:00] Introduction to Leanne Fitzpatrick
[04:23] Write us your suggestions!
[05:43] Tri-pawed dog called Seaweed!
[08:43] How to architect data teams
[14:44] Organizational deficiencies
[19:19] Tensions and conflicts for starters
[24:07] Misunderstandings from marketing
[25:59] The Middle Layer
[28:48] Data science work at publications
[31:11] Mystique of going to real-time
[35:29] Third parties with fraud
[37:40] Augmenting data practitioners with third-party tools
[41:00] Principle of reinventing the wheel and avoiding undifferentiated heavy lifting  
[46:29] Different Abstraction Layer recommendations
[48:42] RN Production
[51:56] Will Python eats RN Production away?
[56:05] Julia as a dark horse
[56:39] Future of RN Production
[58:00] Rapid fire questions