Why would a data scientist use Kafka Jupyter Python KSQL TensorFlow all together in a single notebook?
There is an impedance mismatch between model development using Python and its Machine Learning tool stack and a scalable, reliable data platform. The former is what you need for quick and easy prototyping to build analytic models. The latter is what you need to use for data ingestion, preprocessing, model deployment and monitoring at scale. It…Continue
Added by Kai Waehner on January 22, 2019 at 10:00am — No Comments
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.…Continue
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 …Continue
Added by Kai Waehner on July 8, 2018 at 4:26pm — No Comments
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…Continue
Added by Kai Waehner on May 8, 2018 at 9:30pm — No Comments
Summary: Performance comparison for the popular Deep Learning frameworks supported by Keras – TensorFlow, CNTK, MXNet and Theano
If there are any doubts in regards to the popularity of Keras among the Data Scientist/Engineer community and the mindshare it commands, you just need to look at the support it has been receiving from all major AI and Cloud players. Currently the official Keras release already supports Google's TensorFlow and Microsoft's CNTK deep…Continue
Want to learn machine learning? Looking for data science tutorials and guides to help you master your data and produce actionable, game-changing insights?
Look no further than this list of machine learning eBooks from the Packt team....
Added by Richard Gall on July 21, 2017 at 6:00am — No Comments
Summary: Quantum computing is already being used in deep learning and promises dramatic reductions in processing time and resource utilization to train even the most complex models. Here are a few things you need to know.
Added by William Vorhies on June 13, 2017 at 8:00am — No Comments
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.
Deep Learning is the modern buzzword for artificial neural networks, one of many concepts…Continue
Last year before Christmas at Hochschule München, Fakultät für Informatik and Mathematik I presented about Deep Learning (nbviewer, github, pdf).
Mainly concepts (what's…Continue
UPDATE: Mar 20, 2016 - Added my new follow-up course on Deep Learning, which covers ways to speed up and improve vanilla backpropagation: momentum and Nesterov momentum, adaptive learning rate algorithms like AdaGrad and RMSProp, utilizing the GPU on AWS EC2, and stochastic batch gradient descent. We look at TensorFlow and Theano starting from the basics - variables, functions, expressions, and simple optimizations - from there, building a neural network seems simple! …Continue
Google recently open sourced TensorFlow providing access to a powerful machine learning system. TensorFlow is a machine learning library with tools for data scientists to design intelligent systems (interface for expressing machine learning algorithms and…Continue