Presented By
O’Reilly + Cloudera
Make Data Work
29 April–2 May 2019
London, UK
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Unleashing Apache Kafka and TensorFlow in hybrid architectures

Kai Wähner (Confluent)
12:05–12:45 Thursday, 2 May 2019
Data Engineering and Architecture, Expo Hall
 Location: Expo Hall 2 (Capital Hall N24)
Secondary topics:  AI and Data technologies in the cloud, Model lifecycle management
Average rating: ****.
(4.75, 8 ratings)
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Who is this presentation for?

  • Data scientists, software architects, consultants, developers, and project leads

Level

Beginner

Prerequisite knowledge

  • Experience with open source frameworks for machine learning, messaging, or integration (useful but not required)

What you'll learn

  • Learn how the public cloud allows extreme scale for building analytic models and how the Apache Kafka open source ecosystem enables building a cloud-independent infrastructure for preprocessing and ingestion of data and inference and monitoring of analytic models in real time
  • Understand why hybrid architectures and local model deployment are key for success in many scenarios and why you need a flexible machine learning architecture that supports different technologies and frameworks

Description

How do you leverage the flexibility and extreme scale of the public cloud and the Apache Kafka ecosystem to build scalable, mission-critical machine learning infrastructures that span multiple public clouds—or bridge your on-premises data center to the cloud?

Join Kai Wähner to learn how to use technologies such as TensorFlow with Kafka’s open source ecosystem for machine learning infrastructures. You’ll learn how to build a scalable, mission-critical machine learning infrastructure for data ingestion and processing, model training, deployment, and monitoring.

The discussed architecture includes capabilities like scalable data preprocessing for training and predictions, a 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.

Photo of Kai Wähner

Kai Wähner

Confluent

Kai Wähner is a technology evangelist at Confluent. Kai’s areas of expertise include big data analytics, machine learning, deep learning, messaging, integration, microservices, the internet of things, stream processing, and blockchain. He’s regular speaker at international conferences such as JavaOne, O’Reilly Software Architecture, and ApacheCon and has written a number of articles for professional journals. Kai also shares his experiences with new technologies on his blog.