Welcome to our Blog - Paytm Pipeline

We take pride in the work that we do. We take equal amount of pride in sharing our learnings. Feel free to reach out using the contact form if you would like to learn more on any of our posts. We'd be happy to put you in touch with the post author.

Real-time Personalization Engine – Production and Deployment

This is the third blog post in our blog series where we explain the physical infrastructure for our real-time personalization engine and how we deploy, manage, and monitor our infrastructure. Production Environment In designing and building our production environment...

Real-time Personalization Engine – Architecture and Technology Stack

This is the second blog in our blog series explaining how we serve millions of recommendations to Paytm users everyday. In this blog we are giving you a view into the architecture of the personalization engine along with sharing some learnings for tech choices. System...

Real-time Personalization Engine Blog Series – Teaser

Our previous posts provided an overview of the machine learning models that power the recommendations at Paytm and the improvement we achieved with a semi-deep learning model.  This series of blog posts focuses on how our data and platform engineers built the systems...

A Journey to Semi-Deep Learning at Paytm

At Paytm we focus on our customers above all else. When the customer is happy, we are happy. The Personalization team takes care of user satisfaction by ensuring that we are serving them the right product at the right time and location. For those of you who have read...

Data Plumbing

Paytm, like any e-commerce company, has lots of data to process, moving data across different systems of varying complexity. A traditional enterprise would have various IT groups handling the process of connecting new and old systems. They meet, plan, discuss, plan...

Recommendations at Paytm

When we were asked to build a recommender system for our marketplace, the first thought was to use an out-of-box implementation from spark MLlib like ALS (alternating least square based matrix factorization) or an equivalent off-the-shelf solution. However, as we...

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