Data & Machine Learning at 90 Seconds

827edc42d80fceca858a1603738385b4?s=47 Dat Le
February 26, 2019
Technology
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Data & Machine Learning at 90 Seconds

BigData.SG & Hadoop.SG meetup - Feb 26, 2019

827edc42d80fceca858a1603738385b4?s=128

Dat Le

February 26, 2019
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Transcript

  1. 1.

    DATA AND MACHINE LEARNING AT 90 SECONDS @lenguyenthedat .
 Director

    - Data Science & Engineering at 90 Seconds
 BigData.SG & Hadoop.SG meetup @ AWS
  2. 2.

    90 Seconds in 90 seconds 1. INTRODUCTION

  3. 3.

    Cloud Video Creation Platform Backed by Sequoia since 2016 Trusted

    by the world’s biggest brands
  4. 4.

    90 Seconds is a team of over 180 people from

    18 nationalities across 7 global bases, including Singapore, Auckland, Sydney, Tokyo, London, Berlin and San Francisco. Where we are located
  5. 5.

    2. DATA AT 90 SECONDS

  6. 6.

    THE DATA TEAM AT 90 SECONDS Engineering Data Marketing Finance

    Product Talent Sales Operation Customer Experience
  7. 7.

    THE DATA TEAM AT 90 SECONDS Data Engineer Data Analyst

    Machine Learning Engineer DATA WAREHOUSE DATA PIPELINES INFRASTRUCTURE INTEGRATIONS MACHINE LEARNING, DATA PIPELINES INTEGRATIONS BUSINESS INTELLIGENCE, DATA PIPELINES, VISUALIZATION, INSIGHTS
  8. 8.

    THE DATA STACK AT 90 SECONDS

  9. 9.
    None
  10. 10.

    3. CASE STUDY: VIDEO SEARCH ENGINE

  11. 11.

    CASE STUDY: VIDEO SEARCH ENGINE Videos in, Data out! We

    have produced 30,000 videos till date
  12. 12.

    CASE STUDY: VIDEO SEARCH ENGINE WHAT IF THERE IS A

    SEARCH ENGINE THAT ALLOWS INTERNAL STAFF TO SEARCH FOR VIDEOS
 BY THEIR ENTITIES?
  13. 13.

    CASE STUDY: VIDEO SEARCH ENGINE

  14. 14.

    CASE STUDY: VIDEO SEARCH ENGINE

  15. 15.

    CASE STUDY: VIDEO SEARCH ENGINE

  16. 16.

    CASE STUDY: VIDEO SEARCH ENGINE

  17. 17.

    CASE STUDY: VIDEO SEARCH ENGINE

  18. 18.

    4. FOOD FOR THOUGHT: 
 HOW LONG DID IT TAKE

    FOR YOUR MACHINE LEARNING MODEL TO GO LIVE ON PRODUCTION? one of the most important metric of a successful data team
  19. 19.

    1. FIND THE RIGHT TALENTS AND TEAM COMPOSITION

  20. 20.

    2. DATA INFRASTRUCTURE. BUILD IT FIRST!

  21. 21.

    3. COMMUNICATION & PRIORITIZATION
 BUY-IN AND UPWARDS MANAGEMENT

  22. 22.

    4. DO NOT OVERCOOK. GO FOR AN MVP AND ITERATE!

  23. 23.

    THANK YOU!