Overview:

To learn to design ML systems, it’s helpful to read case studies to see how actual teams deal with different deployment requirements and constraints. Many companies—Airbnb, Lyft, Uber, and Netflix, to name a few—run excellent tech blogs where they share their experience using ML to improve their products and/or processes.Below is a collection of such case studies and a brief overview of them followed by link for each of the articles for further reference.

Using Machine Learning to Predict Value of Homes On Airbnb

✅Robert Chang, Airbnb Engineering & Data Science, 2017
In this detailed and well-written blog post, Chang described how Airbnb used machine learning to predict an important business metric: the value of homes on Airbnb. It walks you through the entire workflow: feature engineering, model selection, prototyping, moving prototypes to production. It's completed with lessons learned, tools used, and code snippets too.

Using Machine Learning to Improve Streaming Quality at Netflix

✅Chaitanya Ekanadham, Netflix Technology Blog, 2018
As of 2018, Netflix streams to over 117M members worldwide, half of those living outside the US. This blog post describes some of their technical challenges and how they use machine learning to overcome these challenges, including to predict the network quality, detect device anomaly, and allocate resources for predictive caching.

150 Successful Machine Learning Models: 6 Lessons Learned at Booking.com

✅Bernardi et al., KDD, 2019
As of 2019, Booking.com has around 150 machine learning models in production. These models solve a wide range of prediction problems (e.g. predicting users’ travel preferences and how many people they travel with) and optimization problems (e.g.optimizing the background images and reviews to show for each user). Adrian Colyer gave a good summary of the six lessons learned here:

• Machine learned models deliver strong business value.
• Model performance is not the same as business performance.
• Be clear about the problem you’re trying to solve.
• Prediction serving latency matters.
• Get early feedback on model quality.
• Test the business impact of your models using randomized controlled trials.

How we grew from 0 to 4 million women on our fashion app, with a vertical machine learning approach

✅Gabriel Aldamiz, HackerNoon, 2018
To offer automated outfit advice, Chicisimo tried to qualify people's fashion taste using machine learning. Due to the ambiguous nature of the task, the biggest challenges are framing the problem and collecting the data for it, both challenges are addressed by the article. It also covers the problem that every consumer app struggles with: user retention.

Machine Learning-Powered Search Ranking of Airbnb Experiences

✅Mihajlo Grbovic, Airbnb Engineering & Data Science, 2019
This article walks you step by step through a canonical example of the ranking and recommendation problem. The four main steps are system design, personalization, online scoring, and business aspect. The article explains which features to use, how to collect data and label it, why they chose Gradient Boosted Decision Tree, which testing metrics to use, what heuristics to take into account while ranking results, how to do A/B testing during deployment. Another wonderful thing about this post is that it also covers personalization to rank results differently for different users.

From shallow to deep learning in fraud

✅Hao Yi Ong, Lyft Engineering, 2018
Fraud detection is one of the earliest use cases of machine learning in the industry. This article explores the evolution of fraud detection algorithms used at Lyft. At first, an algorithm as simple as logistic regression with engineered features was enough to catch most fraud cases. Its simplicity allowed the team to understand the importance of different features. Later, when fraud techniques have become too sophisticated, more complex models are required. This article explores the tradeoff between complexity and interpretability, performance and ease of deployment.

Space, Time and Groceries

✅Jeremy Stanley, Tech at Instacart, 2017
Instacart uses machine learning to solve the task of path optimization: how to most efficiently assign tasks for multiple shoppers and find the optimal paths for them. The article explains the entire process of system design, from framing the problem, collecting data, algorithm and metric selection, topped with a tutorial for beautiful visualization.

Creating a Modern OCR Pipeline Using Computer Vision and Deep Learning

✅Brad Neuberg, Dropbox Engineering, 2017
An application as simple as a document scanner has two distinct components: optical character recognition and word detector. Each requires its own production pipeline, and the end-to-end system requires additional steps for training and tuning. This article also goes into detail the team’s effort to collect data, which includes building their own data annotation platform.

Scaling Machine Learning at Uber with Michelangelo

✅Jeremy Hermann and Mike Del Balso, Uber Engineering, 2019
Uber uses extensive machine learning in their production, and this article gives an impressive overview of their end-to-end workflow, where machine learning is being applied at Uber, and how their teams are organized.

Spotify’s Discover Weekly: How machine learning finds your new music

✅Umesh .A Bhat, 2017
To create Discover Weekly, there are three main types of recommendation models that Spotify employs:

• Collaborative Filtering – Bolivia,Argentina,Chile
• Natural Language Processing – KazakhstanSaudi Arabia,Indonesia,Israel,Turkey,Oman,Nepal,Iraq
• Audio – Honduras,Panama,El Salvador,Guatemala,Dominican Republic

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