How Playtika delivers large-scale predictions in real-time with streaming endpoints

How Playtika delivers large-scale predictions in real-time with streaming endpoints

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Learn how Playtika – a leader in the gaming-entertainment industry – deployed large-scale and real-time predictions while increasing successful model throughput by up to 50% and reduced latency and errors to 0 with streaming endpoints on cnvrg.io. In this webinar we will learn how cnvrg.io customer, Playtika, delivers an exceptional, personalized gameplay experience to over 10 million daily active users with real-time machine learning applications. 

In this webinar we’ll be joined by special guest, Avi Gabay, Director of Architecture at Playtika, where he will discuss how he worked with cnvrg.io to optimize their high volume ML deployments using Apache Kafka and created adaptive game environments for millions of players. See how cnvrg.io helps to solve key MLOps problems and ML production scalability for Playtika with one click streaming endpoints. 

What you’ll learn: 

  • How to decide between Web Services vs. Kafka Streams for your ML models
  • The best way to manage high-volume data and real-time deployments
  • How to set up the ideal architecture for Producer/Consumer interface endpoint
  • How to leverage Apache Kafka and AWS Kinesis for producer/consumer interface
  • How to quickly update models in production with 0 downtime using Kubernetes and Apache Kafka automation

 

Learn how Playtika – a leader in the gaming-entertainment industry – deployed large-scale and real-time predictions while increasing successful model throughput by up to 50% and reduced latency and errors to 0 with streaming endpoints on cnvrg.io. In this webinar we will learn how cnvrg.io customer, Playtika, delivers an exceptional, personalized gameplay experience to over 10 million daily active users with real-time machine learning applications. 

 

In this webinar we’ll be joined by special guest, Avi Gabay, Director of Architecture at Playtika, where he will discuss how he worked with cnvrg.io to optimize their high volume ML deployments using Apache Kafka and created adaptive game environments for millions of players. See how cnvrg.io helps to solve key MLOps problems and ML production scalability for Playtika with one click streaming endpoints. 

 

What you’ll learn: 

  • How to decide between Web Services vs. Kafka Streams for your ML models
  • The best way to manage high-volume data and real-time deployments
  • How to set up the ideal architecture for Producer/Consumer interface endpoint
  • How to leverage Apache Kafka and AWS Kinesis for producer/consumer interface
  • How to quickly update models in production with 0 downtime using Kubernetes and Apache Kafka automation
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