Building Your Own Kaggle Machine

In 2014, I shared the specifications of a 6-core 64GB RAM desktop system that I purchased at around $2,000. Since then, I added NVidia Titan X to it for deep learning at additional $1,000, and it served me well.

However, as other team members started joining me on data science competitions and deep learning competitions got more popular, my team decided to build a more powerful desktop system.

The specifications of the new system that we built are as follows:

  • CPU: Xeon 2.4GHz 14-Core
  • RAM: 128GB DDR4-2400
  • GPU: 4 NVidia 1080 Ti 11GB
  • SSD: 960GB
  • HDD: 4TB 7200RPM
  • PSU: 1600W 80+ Titanium certified

Total cost including tax and shipping was around $7,000. Depending on the budget, you can go down to 2 (-$1,520) 1080 Ti GPU cards instead of 4, or 64GB (-$399) instead of 128GB RAM, and still have a decent system.

You can find the full part lists here.

Additional Resources

Kaggler. Data Scientist.

Winning Data Science Competitions – Latest Slides


This year I had several occasions to give my “Winning Data Science Competitions” talk – at Microsoft, KSEA-SWC 2017, USC Applied Statistics Club, Spark SC, and Whisper.

I am grateful for all these opportunities to share what I enjoy with the data scientist community.

I truly believe that working on competitions on a regular basis can make us better data scientists. Hope my talk and slides help other data scientists.

My talk is outlined as follows:

  1. Why compete
    1. For fun
    2. For experience
    3. For learning
    4. For networking
  2. Data science competition intro
    1. Competitions
    2. Structure
    3. Kaggle
  3. Misconceptions of data science competitions
    1. No ETL?
    2. No EDA?
    3. Not worth it?
    4. Not for production?
  4. Best practices
    1. Feature engineering
    2. Diverse algorithms
    3. Cross validation
    4. Ensemble
    5. Collaboration
  5. Personal tips
  6. Additional resources

You can find latest slides here:

Kaggler. Data Scientist.