MakeMyTrip is India’s #1 online travel platform having more than 70% of the traffic from mobile apps embarked on a journey to revolutionize its customer experience by building a scalable, personalized, machine learning based platform which powers onboarding, in-funnel and post-funnel engagement flows, such as ranking, dynamic pricing, persuasions, cross-sell and propensity models.
See how Lyft and Datatonic are using Apache Flink, Apache beam and python in stream processing, machine learning and analytics.
Thomas Weise a Software Engineer, Streaming Platform at Lyft shares with you vital information on gains attained using Beam technology. You will learn how Lyft has contributed towards the realization of the cross-language support and enabled processing pipelines written in Python to run on Apache Flink.
OLX produces about 50 millions messages daily to be delivered to 300+ millions users across the globe; via email, sms or push. The majority of these notifications relies on the processing of the billions of events generated by their web and mobile platforms to understand the users behaviour and to craft relevant messages designed to influence the customer journey positively.
In this presentation Emanuele Bardelli discusses the approach, challenges and learnings of migrating OLX's notification platform from a monolithic, batch system based on AWS Redshift, SQL and ETL pipelines to a micro-service, real-time system developed with Apache Spark and Python.
Learn how Capital One uses AWS Managed AD to provide highly available authentication and authorization services for its Windows workloads, such as Amazon RDS for SQL Server. This talk is detailed on how Capital One uses Lambda, Python, and PowerShell with cross-account AWS Identity and Access Management (IAM) roles to automate directory deployment across AWS accounts. Also, this talk covers the best practices for integrating AWS Managed AD with your on-premises domain securely, and shows you how to automate the joining of AWS resources to your managed domain.
Airbnb has a wide variety of ML problems ranging from models on traditional structured data to models built on unstructured data such as user reviews, messages and listing images. The ability to build, iterate on, and maintain healthy machine learning models is critical to Airbnb’s success. Many ML Platforms cover data collection, feature engineering, training, deploying, productionalization, and monitoring but few, if any, do all of the above seamlessly.