MLOps.community

Learning from 150 Successful ML-enabled Products at Booking.com // Pablo Estevez // Coffee Sessions #46

Jul 13, 2021

56:43

Creator website

Highlights

AI Chapters

Episode notes

Machine Learning - What Are Some Strategies That Really Work?

03:33

Is There a Platform for Machine Learning?

02:51

Is There Any Thought of Open Source in the Machine Learning World?

03:18

Introduction

6min

Dos Once Ito in Ol Ca, the Company?

5min

The Multi-Disciplinary Team

2min

Machine Learning - What Are Some Strategies That Really Work?

4min

Machine Learning - The Six Lessons, Right?

6min

How Much Extra Business Will You Get From a Ten % Gain on Remasy?

5min

Machine Learning

2min

Is It a Business or a Reelser?

4min

How to Write a Valid, Asetical Valuable Report

3min

10.

Is There a Platform for Machine Learning?

3min

11.

What Metric Should I Be Monitoring?

4min

12.

Using a Common Infrastritor in a Business Case

4min

13.

How Latency Becomes a Challenge?

3min

14.

Is There Any Thought of Open Source in the Machine Learning World?

3min

15.

Line Learning - What Is It?

2min

16.

How to Support the Plasform Rise?

3min

Coffee Sessions #46 with Pablo Estevez, What We Learned from 150 Successful ML-enabled Products at Booking.com.

//Abstract
While most of the Machine Learning literature focuses on the algorithmic or mathematical aspects of the field, not much has been published about how Machine Learning can deliver meaningful impact in an industrial environment where commercial gains are paramount. We conducted an analysis on about 150 successful customer-facing applications of Machine Learning, developed by dozens of teams in Booking.com, exposed to hundreds of millions of users worldwide, and validated through rigorous Randomized Controlled Trials. Our main conclusion is that an iterative, hypothesis-driven process, integrated with other disciplines was fundamental to build 150 successful products enabled by Machine Learning.

// Bio
Pablo Estevez is the Principal Data Scientist at Booking.com. He has worked on recommendations, personalization, and experimentation across the Booking.com website, as well as as a manager on several machine learning, data science, and product development teams.

// Other Links
Talk on the topic: https://www.youtube.com/watch?v=ljhtfrtuNqw&t=4h24m30s
The paper: https://blog.kevinhu.me/2021/04/25/25-Paper-Reading-Booking.com-Experiences/bernardi2019.pdf

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