Presented By
O’Reilly + Cloudera
Make Data Work
29 April–2 May 2019
London, UK

Unleashing Apache Kafka and TensorFlow in Hybrid Architectures

Kai Wähner (Confluent)
12:0512:45 Thursday, 2 May 2019
Data Engineering and Architecture
Location: Expo Hall 2 (Capital Hall N24)
Secondary topics:  AI and Data technologies in the cloud, Model lifecycle management

Who is this presentation for?

Data Scientists, Software Architects, Consultants, Developers, Project Leads.

Level

Beginner

Prerequisite knowledge

Experience with some open source frameworks (Machine Learning, Messaging, Integration) helpful but not required.

What you'll learn

Key takeaways • Public cloud allows extreme scale for building analytic models • 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 • Hybrid architectures and local model deployment are key for success in many scenarios • One machine learning / deep learning frameworks is typically not sufficient for all use cases – a flexible machine learning architecture supports different technologies and frameworks

Description

How can you leverage the flexibility and extreme scale in the public cloud combined with your Apache Kafka ecosystem to build scalable, mission-critical machine learning infrastructures, which span multiple public clouds or bridge your on-premise data centre to cloud?
This talk will discuss and demo how you can leverage machine learning technologies such as TensorFlow with your Kafka deployments in public cloud to build a scalable, mission-critical machine learning infrastructure for data ingestion and processing, and model training, deployment and monitoring.
The discussed architecture includes capabilities like scalable data preprocessing for training and predictions, combination of different Deep Learning frameworks, data replication between data centres, intelligent real time microservices running on Kubernetes, and local deployment of analytic models for offline predictions.

Photo of Kai Wähner

Kai Wähner

Confluent

Kai Waehner 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 the blockchain. He is 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.

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