Interview: Antonio Magnaghi, TicketMaster on Why Honesty is Key for Analytics Success
We discuss lessons from implementing lambda architecture, impact of Big Data on recommender systems, trends, advice, and more.

At Ticketmaster Antonio is leading key machine learning initiatives on recommendations, predictive user modeling, forecasting and large-scale distributed systems for real time optimization problems. Antonio worked on algorithmic content production and marketplace design at Demand Media. He acquired an extensive background in on-line advertising at Yahoo!/Yahoo! Research and Fox Audience Network/The Rubicon Project. Antonio conducted research on IP networks and network data mining while at Microsoft and Fujitsu Laboratories of America.
He holds four patents and a Ph.D. from the University of Tokyo, Japan.
First part of interview
Here is second part of my interview with him:
Anmol Rajpurohit: Q7. What were the key lessons from your experience of designing and implementing Lambda Architecture?
Antonio Magnaghi: In my experience, being able to implement and deploy our recommendation platform in an incremental manner has been crucial. This has been a key feature that we have exploited about lambda architectures.

AR: Q8. Personalization and Recommendation systems have existed for decades. How have they been impacted in the last few years by the Big Data revolution?
AM: Recommender systems are one solution to a very common problem, that, in more general terms,

AR: Q9. What is the best advice you have got in your career?

One of the most valuable lessons has been to always be completely honest and transparent about data and what emerges from it. This requires commitment to communicate with integrity and evangelize what ML can do and, equally importantly, cannot do. This requires us to constantly strive to remove our own biases and expectations. In my view, this is critical in order to foster learning and discovery.
AR: Q10. What trends do you expect to dominate the field of Personalization and Recommendation over the next 2-3 years?

AR: Q11. What key qualities do you look for when interviewing for Data Science related positions on your team?
AM: The drive to "get dirty" with the data, the desire to handle data sets from the inception of the process and to learn from the data. In my experience, there is a really a lot of value in directly handling data

AR: Q12. Which book (or article) did you read recently and liked? What keeps you busy when you are away from work?

AM: I had bought Boosting by R. Schapire and Y. Freund a while back but did not have a chance to read it until recently. It is a great book from authors who have made seminal contributions to this important learning framework. The book is a very complete presentation. In my free time, I still enjoy coding and working on fun projects.
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