//Abstract

As one of the world's most visible software companies, Atlassian's vast data and deep product suite pose an interesting MLOps challenge, and we're grateful to Geoff for taking us behind the curtain.

//Bio

Geoff is a Principal Data Scientist at Atlassian, the software company behind Jira, Confluence & Trello. He works with the product teams and focuses on delivering smarter in-product experiences and recommendations to our millions of active users by using machine learning at scale. Prior to this, he was in the Customer Support & Success division, leveraging a range of NLP techniques to automate and scale the support function.

Prior to Atlassian, Geoff has applied data science methodologies across the retail, banking, media,  and renewable energy industries. He began his foray into data science as a research astrophysicist, where he studied astronomy from the coldest & driest location on Earth: Antarctica.

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Timestamps:

[00:00] Introduction to Geoff Sims

[01:20] Geoff's background

[04:00] Evolution of ML Ecosystem in Atlassian

[06:50] Figure out by necessity

[08:47] Machine Learning not priority number one and disconnected to MLOps

[11:53] Atlassian being behind or advanced?

[16:38] Serious switch of Atlassian around machine learning

[17:47] What data org did it come from?

[20:00] Consolidation of the stack

[21:21] Tooling - blessing and curse

[24:37] Tackling play out

[29:38] Staying on the same page

[30:48] Priority of needs

[31:55] How did it evolve?

[35:12] Where is Atlassian now?

[40:21] "Architecturally, Tecton is very very similar (to ours), it was just way more mature."

[41:17] What unleashed you to do now?

[41:36] "The biggest thing is independence from a data science perspective. Less reliance and less dependence on an army of engineers to help deploy features and models."

[44:25] Have you bought other tools?

[45:43] "At any given time, there's something that's a bottleneck. Look where the bottleneck is, then fix it and move on to the next thing."  

[48:20] Atlassian bringing a model into production

[50:01] "When we undertake whatever the project is, its days or weeks to go to a prototype rather than months or quarters."

[53:10] "Conceptually, you're struggling walking towards that place because that's the place you want to be. If that's your problem, that's good. That's the promised land."

[54:45] "Using our own tools is paramount because we are customers as well. So we see and feel the pain which helps us identify the problems and understand them."