Build Systems that Drive Business
June 11–12, 2018: Training
June 12–14, 2018: Tutorials & Conference
San Jose, CA

Opportunities and challenges in applying machine learning

Alex Jaimes (Dataminr)
3:40pm–4:20pm Thursday, June 14, 2018
Production Engineering, SRE, and DevOps
Location: LL21 C/D Level: Beginner
Secondary topics: Leadership & Career Growth
Average rating: ****.
(4.67, 3 ratings)

Prerequisite knowledge

  • Familiarity with machine learning (useful but not required)

What you'll learn

  • Learn how to find opportunities to apply ML, the pitfalls in applying it, and the steps required to succeed


There are many opportunities in applying machine learning (ML), whether as an individual developer or in a business. But how do you get started? Alex begins with an overview that separates fact from fiction before sharing processes to find opportunities for applying ML. This includes understanding where ML can have the biggest impact while avoiding common pitfalls. You’ll discover why improvements in processes can significantly outweigh algorithmic improvements as you examine data collection and quality, definitions used (e.g, for labeling), metrics, objective functions, overfitting, and the cost of different types of errors, among others. You’ll leave with a better grasp of applying ML in real-world scenarios, including selecting specific algorithms for different tasks.

Topics include:

  • How to identify data sources and data quality issues
  • How to come up with the right metrics
  • How to deal with different types of errors and understand their impact
  • How to examine and improve processes that impact the application of ML
Photo of Alex Jaimes

Alex Jaimes


Alejandro (Alex) Jaimes is senior vice president of AI and data science at Dataminr. His work focuses on mixing qualitative and quantitative methods to gain insights on user behavior for product innovation. Alex is a scientist and innovator with 15+ years of international experience in research leading to product impact at companies including Yahoo, KAIST, Telefónica, IDIAP-EPFL, Fuji Xerox, IBM, Siemens, and AT&T Bell Labs. Previously, Alex was head of R&D at DigitalOcean, CTO at AiCure, and director of research and video products at Yahoo, where he managed teams of scientists and engineers in New York City, Sunnyvale, Bangalore, and Barcelona. He was also a visiting professor at KAIST. He has published widely in top-tier conferences (KDD, WWW, RecSys, CVPR, ACM Multimedia, etc.) and is a frequent speaker at international academic and industry events. He holds a PhD from Columbia University.