Deep Learning Example: Apache Kafka + Python + Keras + TensorFlow + Deeplearning4j

Posted in Apache Kafka, Big Data, Deep Learning, Integration, Java / JEE, Kafka Streams, Machine Learning, Microservices, Stream Processing on November 27th, 2018 by Kai Wähner

I added a new example to my “Machine Learning + Kafka Streams Examples” Github project:

Python + Keras + TensorFlow + DeepLearning4j + Apache Kafka + Kafka Streams“.

This blog post discusses the motivation and why this is a great combination of technologies for scalable, reliable Machine Learning infrastructures. For more details about building Machine Learning / Deep Learning infrastructures leveraging the Apache Kafka open source ecosystem, check out these two blog posts:

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MQTT + Apache Kafka => Video Recording from Kafka Summit Available

Posted in Apache Kafka, Internet of Things, Kafka Connect, KSQL, Machine Learning, MQTT on October 25th, 2018 by Kai Wähner

Just wanted to share the video recording of my talk at Kafka Summit SF 2018 about Kafka MQTT integration. Like all other talks, it is available for free on the Kafka Summit website. Please click the following link to get there:

MQTT Kafka Video

Processing IoT Data from End to End with MQTT and Apache Kafka

All further information about the integration of MQTT and Apache Kafka is available in another blog post. This includes Github examples, etc. Go here:

Apache Kafka + MQTT = End-to-End IoT Integration (Code, Slides, Video)

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Deep Learning KSQL UDF for Streaming Anomaly Detection of MQTT IoT Sensor Data

Posted in Analytics, Apache Kafka, Big Data, Cloud, Cloud-Native, Confluent, Deep Learning, Integration, Internet of Things, Java / JEE, Kafka Connect, Kafka Streams, KSQL, Machine Learning, Microservices, MQTT, Open Source on August 2nd, 2018 by Kai Wähner

I built a scenario for a hybrid machine learning infrastructure leveraging Apache Kafka as scalable central nervous system. The public cloud is used for training analytic models at extreme scale (e.g. using TensorFlow and TPUs on Google Cloud Platform (GCP) via Google ML Engine. The predictions (i.e. model inference) are executed on premise at the edge in a local Kafka infrastructure (e.g. leveraging Kafka Streams or KSQL for streaming analytics).

This post focuses on the on premise deployment. I created a Github project with a KSQL UDF for sensor analytics. It leverages the new API features of KSQL to build UDF / UDAF functions easily with Java to do continuous stream processing on incoming events.

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Model Serving: Stream Processing vs. RPC / REST with Java, gRPC, Apache Kafka, TensorFlow

Posted in Analytics, Apache Kafka, Big Data, Confluent, Deep Learning, Java / JEE, Kafka Streams, KSQL, Machine Learning, Microservices, Open Source, Stream Processing on July 9th, 2018 by Kai Wähner

Machine Learning / Deep Learning models can be used in different ways to do predictions. My preferred way is to deploy an analytic model directly into a stream processing application (like Kafka Streams or KSQL). You could e.g. use the TensorFlow for Java API. This allows best latency and independence of external services. Several examples can be found in my Github project: Model Inference within Kafka Streams Microservices using TensorFlow, H2O.ai, Deeplearning4j (DL4J).

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Deep Learning at Extreme Scale 
with the Apache Kafka Open Source Ecosystem

Posted in Analytics, Apache Kafka, Big Data, Cloud, Confluent, Deep Learning, Integration, Kafka Connect, Kafka Streams, KSQL, Kubernetes, Machine Learning, Microservices, Open Source on May 9th, 2018 by admin

I had a new talk presented at “Codemotion Amsterdam 2018” this week. I discussed the relation of Apache Kafka and Machine Learning to build a Machine Learning infrastructure for extreme scale.

Long version of the title:

Deep Learning at Extreme Scale (in the Cloud) 
with the Apache Kafka Open Source Ecosystem – How to Build a Machine Learning Infrastructure with Kafka, Connect, Streams, KSQL, etc.

As always, I want to share the slide deck. The talk was also recorded. I will share the video as soon as it was published by the organizer.

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Machine Learning Trends of 2018 combined with the Apache Kafka Ecosystem

Posted in Analytics, Apache Kafka, Apache Spark, Big Data, Business Intelligence, Confluent, Deep Learning, Kafka Streams, KSQL, Kubernetes, Machine Learning, Microservices, Open Source, Stream Processing on February 13th, 2018 by Kai Wähner

At OOP 2018 conference in Munich, I presented an updated version of my talk about building scalable, mission-critical microservices with the Apache Kafka ecosystem and Deep Learning frameworks like TensorFlow, DeepLearning4J or H2O. I want to share the updated slide deck and discuss a few updates about newest trends, which I incorporated into the talk.

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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.

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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.

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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).

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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.

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