Apache Kafka + Machine Learning => Confluent Blog Post and Github Project

Posted in Apache Kafka, Big Data, Confluent, Deep Learning, Kafka Streams, KSQL, Machine Learning, Open Source, Stream Processing on October 27th, 2017 by Kai Wähner

I am happy that my first official Confluent blog post was published and want to link to it from by blog:

How to Build and Deploy Scalable Machine Learning in Production with Apache Kafka

The post explains in detail how you can leverage Apache Kafka and its Streams API to deploy analytic models to a lightweight, but scalable, mission-critical streaming appilcation.

Apache Kafka Ecosystem for Machine Learning

Github Examples for Apache Kafka + Machine Learning

If you want to take a look directly at the source code, go to my Github project about Kafka + Machine Learning. It contains several examples how to combine Kafka Streams with frameworks like TensorFlow, H2O or DeepLearning4J.

Tags: , , , , , , , , , , ,

Deep Learning in Real Time with TensorFlow, H2O.ai and Kafka Streams (Slides from JavaOne 2017)

Posted in Analytics, Apache Kafka, Big Data, Business Intelligence, Confluent, Deep Learning, Docker, Java / JEE, Kafka Streams, Machine Learning, Microservices, Open Source, Stream Processing on October 4th, 2017 by Kai Wähner

Early October… Like every year in October, it is time for JavaOne and Oracle Open World in San Francisco… I am glad to be back at this huge event again. My talk at JavaOne 2017 was all about deployment of analytic models to scalable production systems leveraging Apache Kafka and Kafka Streams. Let’s first look at the abstract. After that I attach the slides and refer to further material around this topic.

Tags: , , , , , , , , , , , , , , , , , , , , ,

Kafka Streams + H2O.ai + TensorFlow (Video Recording / Live Demo)

Posted in Analytics, Apache Kafka, Big Data, Kafka Streams, Machine Learning, Open Source, Stream Processing on September 7th, 2017 by Kai Wähner

I do a lot of presentations these days at meetups and conferences with one focus: How to leverage Apache Kafka and Kafka Streams to apply analytic models (built with H2O, TensorFlow, DeepLearning4J and other frameworks) to scalable, mission-critical environments. As many attendees have asked me, I created a video recording about this talk (focusing on live demos).

Tags: , , , , , , , , , , , , , ,

Why I Move (Back) to Open Source for Messaging, Integration and Stream Processing

Posted in Analytics, API Management, Big Data, Blockchain, Cloud, Cloud-Native, Docker, ESB, Hadoop, Internet of Things, Java / JEE, Machine Learning, Microservices, Middleware, SOA on May 1st, 2017 by Kai Wähner

After three great years at TIBCO Software, I move back to open source and join Confluent, a company focusing on the open source project Apache Kafka to build mission-critical, scalable infrastructures for messaging, integration and streaming analytics. Confluent is a Silicon Valley startup, still in the beginning of its journey, with a 700% growing business in 2016, and is exjustpected to grow significantly in 2017 again.

In this blog post, I want to share why I see the future for middleware and big data analytics in open source technologies, why I really like Confluent, what I will focus on in the next months, and why I am so excited about this next step in my career.

Tags: , , , , , , , , , , , , , , , , , ,

Visual Analytics + Open Source Deep Learning Frameworks

Posted in Analytics, Big Data, Cloud, Hadoop, Machine Learning on April 24th, 2017 by Kai Wähner

Deep Learning gets more and more traction. It basically focuses on one section of Machine Learning: Artificial Neural Networks. This article explains why Deep Learning is a game changer in analytics, when to use it, and how Visual Analytics allows business analysts to leverage the analytic models built by a (citizen) data scientist.

Tags: , , , , , , , , , , , , , , , , , , , , , ,

Comparison: Data Preparation vs. Inline Data Wrangling in Machine Learning and Deep Learning Projects

Posted in Analytics, Big Data, Business Intelligence, Hadoop on February 13th, 2017 by Kai Wähner

I want to highlight a new presentation about Data Preparation in Data Science projects:

“Comparison of Programming Languages, Frameworks and Tools for Data Preprocessing and (Inline) Data Wrangling  in Machine Learning / Deep Learning Projects”

Data Preparation as Key for Success in Data Science Projects

A key task to create appropriate analytic models in machine learning or deep learning is the integration and preparation of data sets from various sources like files, databases, big data storages, sensors or social networks. This step can take up to 80% of the whole project.

Tags: , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,