Presented By
O’Reilly + Cloudera
Make Data Work
29 April–2 May 2019
London, UK
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AI for good at scale in real time: Challenges in machine learning and deep learning

Alex Jaimes (Dataminr)
14:0514:45 Thursday, 2 May 2019
Data Science, Machine Learning & AI, Expo Hall
Location: Expo Hall (Capital Hall N24)
Average rating: ***..
(3.00, 2 ratings)

Who is this presentation for?

  • AI practitioners, data scientists, and engineers



Prerequisite knowledge

  • A working knowledge of machine learning, deep learning, and data science

What you'll learn

  • Understand the challenges faced when applying deep learning and machine learning at scale in real time, particularly when data sources and the data are heterogeneous
  • Explore examples of key decisions in the process of building models and deploying them for a real-world application


When unusual events occur, people use social media platforms to share information. Similarly, data generated by different sensors can indicate that breaking events are taking place. Such data is extremely useful, in particular, for public safety (emergencies, crisis management, etc.): the more knowledge that’s gathered, the better the response can be. When an emergency event is ongoing, that information can be critical in making decisions to keep people safe or take control of the particular situation as it’s unfolding. This includes the type of deployment, locations, and many other factors. From a computational point of view, this entails ingesting thousands of data points per second: sifting through and identifying relevant information from different sources, in different formats, with varying levels of detail, in real time so that first responders and others can be alerted at the right level and at the right time.

Alex Jaimes explains how to apply machine learning and deep learning to create, update, and deploy models to make that possible. Alex describes the major challenges that emerge when addressing the problems outlined above, emphasizing the importance of choices along every step of the way, from training data and labeling to the selection of algorithms and how they’re deployed. Along the way, Alex shares examples that showcase how to apply the lessons learned to a variety of other problems that deal with large-scale heterogeneous datasets in real time—and how that can be leveraged for social good.

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.