Kinshuk Mishra Kinshuk Mishra on

Spotify Tech Lead Kinshuk Mishra and Engineer Noel Cody share their experience about building personalized ad experiences for users through iterative engineering and product development. They explain their process of continuous problem discovery, hypothesis generation, product development and experimentation. Later they deep dive into the specific ad personalization problems Spotify is solving and explain their data infrastructure technology stack in detail. They also speak about how they've experimented various product hypothesis and iteratively evolved their infrastructure to keep up with the product requirements.

Continue
Jeffrey Picard Jeffrey Picard on

Understanding the billions of data points we ingest each month is no easy task. Through the development of models that allow us to do so, we’ve noticed some commonalities in the process of converting raw data to real-world understanding. Although you can get pretty good results with simple models and algorithms, digging beyond the obvious abstractions and using more sophisticated methods requires a lot of effort. In school we often learn different techniques and algorithms in isolation, with neatly fitted input sets, and study their properties. In the real world, however, especially the world of location data, we often need to combine these approaches in novel ways in order to yield usable results.

Continue
Toby Matejovsky Toby Matejovsky on

Tapad Director of Engineering, Toby Matejovsky talks about how his team built and scaled their cross device digital advertising platform that handles over 50,000 queries per second per server with sub-millisecond latency, 95-99% of the time. Toby shares lessons learned, scaling tips and best practices as well as answer questions ranging from tools and technologies to people and processes.

Continue
Claudia Perlich Claudia Perlich on

Here's a new talk on targeted online advertising recorded at one of the NYC Machine Learning meetups. Two presenters from Media6 labs spoke about their respective papers from the recent Knowledge Discover and Data Mining conference (KDD). Claudia Perlich presented "Bid Optimizing and Inventory Scoring in Targeted Online Advertising" and Troy Raeder presented "Design Principles of Massive, Robust Prediction Systems." Full abstracts and audio below.

Continue