From (Big) Data Mess to Data as an Innovation Enabler
summary
OLX Group collects and processes billions of events per day from a heterogeneous portfolio and serves ~150 million unique users; the engineering story shows a streaming framework and client libraries that feed a central data catalog and reservoir on AWS, a reporting stack for multi-tenant access, secure distribution and compliance controls, and an ML pipeline that trains and deploys models with AWS SageMaker and TensorFlow to power search, recommendations and moderation.
AI-written summary · about
chapters (10)
- 0:00Welcome
- 5:53Data mess to innovation
- 10:00Scaling data infrastructure
- 18:26Centralizing data
- 23:05Data cataloging
- 29:09ML and AI in data
- 33:02Data quality insights
- 38:03Machine learning for search and recommendations
- 44:38Democratizing data access
- 47:45Q&A
related talks
58mReal Data Science Examples at OLX's Online Marketplace
29mBuilding ML Platform Capabilities at Global Scale
34mData at scale: How to deal with small challenges when they become massive
22mData Ecosystem to power AI
51mIncreasing Productivity and Reducing Costs in Big Data Ecosystems on AWS
29mBecoming a data-driven enterprise
45mOur way to a data-driven enterprise - A Journey by Bosch
29mSeveral People Are Tuning: Data and Machine Learning at