Building intelligent, real-time applications using Machine Learning

8d943d05291180fae0976a9015e6f73a?s=47 Jayesh
November 24, 2017

Building intelligent, real-time applications using Machine Learning

Discuss the current-state-of-affairs for deploying Machine Learning models
Discuss shortcomings of this approach
Discuss the value of streaming data
Brief introduction to Apache Kafka and Streaming applications
Discuss how to use Apache Kafka to use ML models in real-time
Demonstrate how we use a Demography Prediction model in real-time

8d943d05291180fae0976a9015e6f73a?s=128

Jayesh

November 24, 2017
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Transcript

  1. Scalable, real-time Machine Learning using Apache Kafka

  2. Agenda • Traditional model deployment process • 90 seconds to

    WoW • Let’s process the incoming stream • Demo • What’s more? 2
  3. $ whoami • Personalisation lead at Hotstar • Led Data

    Infrastructure team at Grofers and TinyOwl • Kafka fanboy • Usually rant on twitter @jayeshsidhwani 3
  4. Machine Learning @ Hotstar • ~150 mn users • 4.8

    mn peak concurrency • 120K peak recommendation requests per second • Diverse content in diverse languages 4
  5. Traditional model deployment process 5 Model Training Data Lake Serialized

    Model Batch Predictions Recommendation APIs Offline Online • One-day / few-hours batch pre-compute • Slow time to react
  6. Sense of urgency? 6 • 90 seconds to convert a

    new user • To power his experience, we need to know user’s gender, interests and more • Need an always-thinking machine
  7. Thinking streams 7 Data at Rest Data in motion •

    Slow • Batch-y • Fast • Sub-second
  8. Enter Apache Kafka 8 • Kafka is a scalable, fault-tolerant,

    distributed message queue • Producers and Consumers • Uses ◦ Real-time applications such as: intelligent notifications, anomaly etc. ◦ Asynchronous communication in event-driven architectures Diagram credits: http://kafka.apache.org
  9. Real-time infrastructure at Hotstar 9 • All clickstream data pushed

    into Apache Kafka • Apache Kafka Streams to process events as they happen • Incoming data available for everyone Intelligence Apple TV iOS ANDROID Roku STREAM PROCESSING FRAMEWORK Filter Window Join Anomaly Machine Learning
  10. Demo Predict whether a flight is delayed in real-time 10

  11. How to process a stream? 11 ML

  12. Advanced use-cases 12 page-clicks Processor nodes Source / Sink nodes

    video-plays predict-gender predict-interest 5-min trending videos Recommended for You Hotstar Streaming Platform
  13. Questions? 13 tech.hotstar.com