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Streaming endpoints: real-time machine learning in production with Apache Kafka

Build and deploy large-scale and real-time machine learning to production in one click

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Scalable Streaming Endpoints with Apache Kafka​

Scalable deployment solution for high volume machine learning

  • Optimized machine learning for event-driven, large scale applications
  • Increase successful throughput by up to 50% 
  • Enable Kubernetes backed autoscaling for Kafka streams 
  • Ensure your service can accommodate any spike in incoming demand
Scalable deployment solution for high volume machine learning

Optimize real-time machine learning deployments with zero downtime

  • Execute real time predictions with complex model monitoring features (logs, a/b testing, canary deployment, integration with flows, continual learning and more)
  • Reduce latency and error rates of your models and avoiding complex triggering and scheduling as data comes in
  • Quickly update models in production with zero downtime using Kubernetes and Apache Kafka automation 
  • Enable low-latency streaming of data with zero batching and complete data continuity

Gain better machine learning model computing performance

  • Enable full utilization of your Kubernetes Pods to their optimal deployment 
  • Deliver stable and powerful services that automatically scales with Kubernetes 
  • Solve key DevOps challenges and ML production scalability with high level MLOps