14–17 Oct 2019

Scaling machine learning at Careem

Ahmed Kamal (Careem)
14:3515:15 Wednesday, 16 October 2019
Location: King's Suite - Balmoral
Secondary topics:  Machine Learning tools
Average rating: ****.
(4.00, 7 ratings)

Who is this presentation for?

  • ML engineers, data scientists, product managers, and research scientists

Level

Intermediate

Description

Every day, Careem, the Middle East’s first tech unicorn platform, enables millions of users across 120+ cities to get a safe ride from point A to point B as well as getting food and deliveries on time. The company had to solve a lot of challenges to ensure the delivery of optimized and localized experiences across these domains including choosing the right driver for a booking, estimating the arrival time of the booked car or order, demand and supply forecasting over time for different locations, balancing supply and demand of vehicles across time and locations, and up-front price predictions.

Due to the complexity of the business and the fast-paced nature of its dynamics, thousands of production-level ML models have to be developed, served, and continuously refreshed. Ahmed Kamal explains how Careem is building a scalable and flexible ML platform that facilitates development and enables expeditious deployment of scalable ML services in a cost-efficient way, including examples of ML use cases at Careem currently productionized on top of an ML platform; how to balance between the needs of different users (researchers, data scientists, business users, etc.); how to productionize different ML stages, including dataset generation, model selection, training, tuning and testing, model evaluation, tracking and benchmarking, model serving, and performance monitoring and integration; and the role of ML platforms and AutoML in fostering, democratizing, and accelerating the impact of ML across the organization.

Prerequisite knowledge

  • General knowledge of software engineering
  • A basic understanding of ML

What you'll learn

  • Discover the motivations and challenges of building an ML platform
  • Understand the ML lifecycle and how to productionize different stages in a generic and scalable way that balances flexibility with ease of use
  • Learn the role of AutoML in democratizing and accelerating the impact of AI across organizations
Photo of Ahmed Kamal

Ahmed Kamal

Careem

Ahmed Kamal is the machine learning platform lead at Careem, where he’s working on developing machine learning services and data infrastructure. Previously, he worked on building the data science platform at Seeloz. He’s passionate about building data products that change people’s lives.

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