Speaker: Kai Waehner, Technology Evangelist, Confluent
In this online talk, Technology Evangelist Kai Waehner will discuss and demo how you can leverage technologies such as TensorFlow with your Kafka deployments to build a scalable, mission-critical machine learning infrastructure for ingesting, preprocessing, training, deploying and monitoring analytic models.
He will explain challenges and best practices for building a scalable infrastructure for machine learning using Confluent Cloud on Google Cloud Platform (GCP), Confluent Cloud on AWS and on-premise deployments.
The discussed architecture will include capabilities like scalable data preprocessing for training and predictions, combination of different deep learning frameworks, data replication between data centers, intelligent real-time microservices running on Kubernetes and local deployment of analytic models for offline predictions.
Join us to learn about the following:
-Extreme scalability and unique features of Confluent Cloud
-Building and deploying analytic models using TensorFlow, Confluent Cloud and GCP components such as Google Storage, Google ML Engine, Google Cloud AutoML and Google Kubernetes Engine in a hybrid cloud environment
-Leveraging the Kafka ecosystem and Confluent Platform in hybrid infrastructures
