The Conference for Machine Learning Innovation

Lean Data Science: Tailoring Agile Practices for Data Science Projects

Session
Join the ML Revolution!
Register until April15:
✓Save up to 310 €
✓ 2-in-1 conference special
✓10 % Team Discount
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Join the ML Revolution!
Register until April15:
✓Save up to 310 €
✓ 2-in-1 conference special
✓10 % Team Discount
Register Now
Join the ML Revolution!
Register until December 12:
✓ML Intro Day for free
✓Raspberry Pi or C64 Mini for free
✓Save up to $580
Register Now
Join the ML Revolution!
Register until December 12:
✓ML Intro Day for free
✓Raspberry Pi or C64 Mini for free
✓Save up to $580
Register Now
Join the ML Revolution!
Register until November 7th:
✓Save up to € 210
✓10% Team Discount
Register Now
Join the ML Revolution!
Register until November 7th:
✓Save up to € 210
✓10% Team Discount
Register Now

About 80% of AI projects never make it into production.

Why do they fail? Top factors are misaligned objectives, lack of collaboration between business stakeholders and data scientists, and an inability to leverage the collective knowledge and talents of the entire “team”, including Data Scientists, Data Engineers, ML Ops, etc.

These problems are not unique. Agile practices have become the de-facto approach to deliver software applications effectively. Can we adapt them for data science projects?

It is not that easy. Most data science teams experience problems adapting Agile due to Data Science specifics. For example, they struggle to produce a valuable increment at the end of the sprint, sometimes newly discovered information can ruin a sprint plan right in the middle of it, etc.

We need to tailor Agile Practices to allow for Data Science specifics.

In this talk, we will explore collaborative techniques that guide data science teams in their agile adaption. We will discuss how to come up with nice and clear product hypothesis, how to prioritize them using ICE/RICE method, how to decompose huge AI Epics into a small and easy to validate data science hypothesis, and how to effectively manage work using Kanban and Scrum approaches.

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This Session belongs to the Diese Session gehört zum Programm vom MunichMunich program. Take me to the program of . Hier geht es zum Programm von Singapore Singapore .

This Session belongs to the Diese Session gehört zum Programm vom MunichMunich program. Take me to the program of . Hier geht es zum Programm von Berlin Berlin .

This Session Diese Session belongs to the gehört zum Programm von MunichMunich program. Take me to the current program of . Hier geht es zum aktuellen Programm von Munich Munich , Singapore Singapore or oder Berlin Berlin .

Behind the Tracks