We discussed about how to build and run data teams and engage better with business.
Mike and I take a philosophical detour on Talk Python and discuss life lessons from machine learning.
Design and architecture, tech stack, methodology, results, and lessons learned.
Why did I start writing? What's my writing process? What's the writing culture at Amazon like?
What's an average day like? What's great about the role? How's working in Amazon?
My chat with James Le about my experience, leadership, agile, ML in production, writing, and more.
Comparing baselines (matrix factorization) against novel approaches using graphs & NLP.
In-depth sharing on how to put machine learning systems into production.
Keynote on how Asia's tech giants scale and their SuperApp strategy.
How we built an ML system to predict hospitalization costs at admission; sharing at DATAx Conference.
What's the difference between a data scientist, data engineer, and ML engineer? A panel at Google.
Yes, Agile can be adopted by data science teams. Moderating a panel at GovTech STACK.
Technical challenges easy compared to business and people issues. Sharing at the BDA Summit.
And my idiosyncratic journey to VP of Data Science at Lazada (Alibaba). A Lunchtime chat at INSEAD.
What is data science, how to pick it up, and how to enter the field? A discussion with SMU undergrads.
Sharing about why data science, data science myths, a typical day, and more with TIA.
Tools and skills to pick up and how to practice them. An Invited Talk with Masters in IT candidates.
How Lazada ranks products to improve customer experience and conversion at Strata 2016.
Sharing about my first data science competition at DataScience SG.
20 Jun 2015  ·  1 min  ·  machinelearning
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