Eugene Yan

Part II of the previous write-up, this time on applications and frameworks of Spark in production

Sharing my notes & practical knowledge from the conference for people who don't have the time.

Does DS have business requirements?

After this article, we'll have a workflow of tests and checks that run automatically with each git push.

A curious discussion made me realize my expert blind spot.

Haste makes waste.

Initially, I didn't like it.

Crocker's Law, cognitive dissonance, and how to receive (uncomfortable) feedback better.

Can maintaining machine learning in production be easier?

I thought deploying machine learning was hard.

An expansion of my Twitter thread that went viral.

What I Learnt about evaluating ideas from first-hand participation in a hackathon.

What I learned about measuring diversity, novelty, surprise, and serendipity from 10+ papers.

Why you should give a talk and some tips from five years of speaking and hosting meet-ups.

Should I join a start-up?

Using a Zettelkasten helps you make connections between notes, improving learning and memory.

Writing begins before actually writing; it's a cycle of reading -> note-taking -> writing.

Automate your experimentation workflow to minimize effort and iterate faster.

How hard work, many failures, and a bit of luck got me into the field and up the ladder.

Comparing baselines (matrix factorization) against novel approaches using graphs & NLP.

Beating the baseline using Graph & NLP techniques on PyTorch, AUC improvement of ~21% (Part 2 of 2).

Building a baseline recsys based on data scraped off Amazon.

OMSCS CS6200 (Introduction to OS) - Moving data from one process to another, multi-threaded.

In-depth sharing on how to put machine learning systems into production.

Keynote on how Asia's tech giants scale and their SuperApp strategy.

OMSCS CS6750 (Human Computer Interaction) - You are not your user!

Moving off wordpress and hosting for free on GitHub.

OMSCS CS6440 (Intro to Health Informatics) - A primer on key tech and standards in healthtech.

OMSCS CS7646 (Machine Learning for Trading) - Don't sell your house to trade algorithmically.

No, you don't need a PhD or 10+ years of experience.

How we built an ML system to predict hospitalization costs at admission; sharing at DATAx Conference.

Taking the best from agile and modifying it to fit the data science process (Part 2 of 2).

A deeper look into the strengths and weaknesses of Agile in Data Science projects (Part 1 of 2).

What's the difference between a data scientist, data engineer, and ML engineer?

OMSCS CS6601 (Artificial Intelligence) - First, start with the simplest solution, and then add intelligence.

Yes, Agile can be adopted by data science teams.

OMSCS CS6460 (Education Technology) - How to scale education widely through technology.

OMSCS CS7642 (Reinforcement Learning) - Landing rockets (fun!)

Technical challenges easy compared to business and people issues.

Culture >> Hierarchy, Process, Bureaucracy.

And my idiosyncratic journey to VP of Data Science at Lazada (Alibaba).

OMSCS CS7641 (Machine Learning) - Revisiting the fundamentals and learning new techniques.

How being a Lead / Manager is different from being an individual contributor.

What is data science, how to pick it up, and how to enter the field?

OMSCS CS6300 (Software Development Process) - Java and collaboratively developing an Android app.

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.

Tools and skills to pick up, and how to practice them.

OMSCS CS6476 Computer Vision - Performing computer vision tasks with ONLY numpy.

If things are not failing, you're not innovating enough. - Elon Musk