Building a Lean AI Startup – Lessons Learned | MadKudu
Get the slides: https://www.datacouncil.ai/talks/building-a-lean-ai-startup-lessons-learned
ABOUT THE TALK
With recent developments in open source tools and cloud infrastructure, it has become easier to build Data Engineering applications. Lean Startup methodologies and MVPs have taken over product development. But can you apply them to building an AI product?
How do you solve the Catch-22 of finding the data you need to get started? How do you iterate quickly (and cheaply) to a model that creates value for customers? What is an MVP in AI? How do you keep experimenting fast while ensuring the reliability of your data pipelines? With a team size that's a rounding error at Uber or AirBnb?
In this talk, we'll share some of the issues we encountered trying to apply "lean startup" techniques to the development of a pure-play ML product and the techniques and tools we used to circumvent these difficulties.
ABOUT THE SPEAKER
Paul is the co-founder and CTO of MadKudu, a company using Machine Learning to optimize customer journeys at scale. Prior to MadKudu, he was a Product Manager in charge of the data platform at AgilOne. For reasons that made sense at the time, Paul holds MS Degrees in Nuclear Engineering from UC Berkeley and Ecole Polytechnique.
ABOUT DATA COUNCIL:
Data Council (https://www.datacouncil.ai/) is a community and conference series that provides data professionals with the learning and networking opportunities they need to grow their careers. Make sure to subscribe to our channel for more videos, including DC_THURS, our series of live online interviews with leading data professionals from top open source projects and startups.
FOLLOW DATA COUNCIL:
Twitter: https://twitter.com/DataCouncilAI
LinkedIn: https://www.linkedin.com/company/datacouncil-ai
Facebook: https://www.facebook.com/datacouncilai
Eventbrite: https://www.eventbrite.com/o/data-council-30357384520
Get the slides: https://www.datacouncil.ai/talks/building-a-lean-ai-startup-lessons-learned
ABOUT THE TALK
With recent developments in open source tools and cloud infrastructure, it has become easier to build Data Engineering applications. Lean Startup methodologies and MVPs have taken over product development. But can you apply them to building an AI product?
How do you solve the Catch-22 of finding the data you need to get started? How do you iterate quickly (and cheaply) to a model that creates value for customers? What is an MVP in AI? How do you keep experimenting fast while ensuring the reliability of your data pipelines? With a team size that’s a rounding error at Uber or AirBnb?
In this talk, we’ll share some of the issues we encountered trying to apply “lean startup” techniques to the development of a pure-play ML product and the techniques and tools we used to circumvent these difficulties.
ABOUT THE SPEAKER
Paul is the co-founder and CTO of MadKudu, a company using Machine Learning to optimize customer journeys at scale. Prior to MadKudu, he was a Product Manager in charge of the data platform at AgilOne. For reasons that made sense at the time, Paul holds MS Degrees in Nuclear Engineering from UC Berkeley and Ecole Polytechnique.
ABOUT DATA COUNCIL:
Data Council (https://www.datacouncil.ai/) is a community and conference series that provides data professionals with the learning and networking opportunities they need to grow their careers. Make sure to subscribe to our channel for more videos, including DC_THURS, our series of live online interviews with leading data professionals from top open source projects and startups.
FOLLOW DATA COUNCIL:
Twitter: https://twitter.com/DataCouncilAI
LinkedIn: https://www.linkedin.com/company/datacouncil-ai
Facebook: https://www.facebook.com/datacouncilai
Eventbrite: https://www.eventbrite.com/o/data-council-30357384520
