Experiment

What I Learned From Arize:Observe 2022

What I Learned From Arize:Observe 2022

Last month, I had the opportunity to speak at Arize:Observe, the first conference dedicated solely to ML observability from both a business and technical perspective. More than a mere user conference, Arize:Observe features presentations and panels from industry thought leaders and ML teams across sectors. Designed to tackle both the basics and most challenging questions and use cases, the conference has sessions about performance monitoring and troubleshooting, data quality and drift monitoring and troubleshooting, ML observability in the world of unstructured data, explainability, business impact analysis, operationalizing ethical AI, and more.

In this blog recap, I will dissect content from the summit’s most insightful technical talks, covering a wide range of topics from scaling real-time ML and best practices of effective ML teams to challenges in monitoring production ML pipelines and redesigning ML platform.

Datacast Episode 65: Chaos Theory, High-Frequency Trading, and Experimentations at Scale with David Sweet

Datacast Episode 65: Chaos Theory, High-Frequency Trading, and Experimentations at Scale with David Sweet

David Sweet was a quantitative trader at GETCO, where he used experimental methods to tune trading strategies, and a machine learning engineer at Instagram, where he experimented on a large-scale recommender system. He is currently writing a book called "Tuning Up," an extension of lectures given at NYU Stern on tuning high-frequency trading systems. Before working in the industry, he received a Ph.D. in Physics and published research in Physical Review Letters and Nature. The latter publication – an experiment demonstrating chaos in geometrical optics -- has become a source of inspiration for computer graphics artists, undergraduate Physics instructors, and an exhibit called TetraSphere at the Museum of Mathematics in New York City.

Recommendation System Series Part 8: The 14 Properties To Take Into Account When Evaluating Real-World Recommendation Systems

Recommendation System Series Part 8: The 14 Properties To Take Into Account When Evaluating Real-World Recommendation Systems

Various properties are commonly considered when choosing the recommendation approach, whether for offline or online scenarios. These properties have trade-offs, so it is critical to understand and evaluate their effects on the overall performance and the user experience. This blog post is my attempt to summarize these properties succinctly.

Datacast Episode 6: Data Science in the Travel Industry with Ewan Nicolson

Datacast Episode 6: Data Science in the Travel Industry with Ewan Nicolson

Ewan Nicolson has been working professionally with numbers and computers for the past 13 years. During the past 6 years at Skyscanner, he has seen the data team grow from 2 analysts based in Edinburgh to a global team of 25+ data scientists making a global impact.