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How to succeed with
Machine Learning
Artūrs Valujevs, Data Scientist
2018.01.25
Outline
• Hype of the year!
• Harsh reality
• Pushing the expectations
• The equation of success
• Different perspective
•...
Hype of the year!
• We want an AI system!
• In a container!
• On a cloud!
• Performing distributed unsupervised computing!...
Harsh reality
All the companies
Got «Big Data» going
Hired Data Scientist
Deployed project
Pushing the expectations
The equation for success
• ML – Your machine learning solution
• DS – Data Science
• DG – Data Governance
• IC – Infrastru...
Different perspective
Question Data Infrastructure
Machine
learning
Reason Evidence Means Tools
or more like…
What is the right question?
Event OutcomeConditions
time
We need completely automated AI
solution for our core business fu...
Curse of mathematics
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“How to Succeed with Machine Learning” by Arturs Valujevs from Intrum Global Technologies at Machine Learning focused 62nd DevClub.lv

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There is certainly a growing demand for incorporating machine learning solutions into various types of business. Yet, the knowledge base is not always keeping up with hype around this subject. Some reports [source: Global CIO Point of View] tell us that 9 out of 10 CIOs plan to use machine learning solutions to achieve certain goals in their companies. Yet only around 20% actually have something in production and only 5% use machine learning extensivelly. Why? There are quite a few reasons, and that’s what this talk is all about.
Arturs is a data scientist in Intrum Global Technologies, has experience in developing machine learning solutions ranging from scoring to automated self learning systems.

Published in: Technology
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“How to Succeed with Machine Learning” by Arturs Valujevs from Intrum Global Technologies at Machine Learning focused 62nd DevClub.lv

  1. 1. How to succeed with Machine Learning Artūrs Valujevs, Data Scientist 2018.01.25
  2. 2. Outline • Hype of the year! • Harsh reality • Pushing the expectations • The equation of success • Different perspective • What is the right question? • Curse of mathematics
  3. 3. Hype of the year! • We want an AI system! • In a container! • On a cloud! • Performing distributed unsupervised computing! • Operating through the blockchain! (oh my..) Translation: We want to optimize our operation costs by employing our data.
  4. 4. Harsh reality All the companies Got «Big Data» going Hired Data Scientist Deployed project
  5. 5. Pushing the expectations
  6. 6. The equation for success • ML – Your machine learning solution • DS – Data Science • DG – Data Governance • IC – Infrastructure Capabilities • AM – Analytical Maturity • R – Literally everything that can go wrong ML = DS * DG * IC * AM * R
  7. 7. Different perspective Question Data Infrastructure Machine learning Reason Evidence Means Tools or more like…
  8. 8. What is the right question? Event OutcomeConditions time We need completely automated AI solution for our core business function!
  9. 9. Curse of mathematics

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