Building Better Data Teams // Leanne Fitzpatrick // Coffee Sessions #113
MLOps.community
Is There a Software Bug?
What is a piece of technology that you are bullish on that might surprise people? What do you want to be remembered for when you leave the planet? And how would you like your tombstone look.
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 may not have spent 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 get 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.
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// 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 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 eat RN Production away?
[56:05] Julia as a dark horse
[56:39] Future of RN Production
[58:00] Rapid-fire questions
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