In die Kategorie Analytics fallen sowohl die klassischen Data-driven-Business / BI-Themen (Data Warehouse, ETL, Reporting, Dashboards) als auch die neueren Trends in diesem Umfeld: Big Data, Data Science & Deep Learning und Search-based Applications.

Wir verstehen uns als Spezialist für anspruchsvolle Aufgaben in den Bereichen Data Management und Analytics, die unter Zeitdruck gelöst werden müssen und für die oftmals in den Unternehmen keine eigenen Fachleute verfügbar sind:

  • die Modellierung hochkomplexer Cubes,
  • die Integration heterogener Datenquellen,
  • der effiziente Umgang mit sehr großen Datenvolumina (Big Data),
  • die wissenschaftliche Analyse dieser Daten-Pools (Data Science) und
  • der Einsatz von innovativen Suchtechnologien im Unternehmenskontext.

From Exploration to Production — Bridging the Deployment Gap for Deep Learning

2018-10-01T14:49:13+00:00

This article introduces EMNIST, we develop and train models with PyTorch, translate them with the Open Neural Network eXchange format ONNX and serve them through GraphPipe. We will orchestrate these technologies to solve the task of image classification using the more challenging and less popular EMNIST dataset.

This is the first part of a series of two blogposts on deep learning model exploration, translation, and deployment. Both involve many technologies like PyTorch, Tens

From Exploration to Production — Bridging the Deployment Gap for Deep Learning 2018-10-01T14:49:13+00:00

Time Series Forecasting with Machine Learning Models

2018-09-13T11:38:23+00:00

In this article we explain how time series forecasting tasks can be solved with machine learning models, starting with the problem modeling and ending with visualizing the results by embedding the models in a web app for demonstration purposes.

Recently, Machine Learning (ML) models have been widely discussed and successfully applied in time series forecasting tasks (Bontempi et al., 2012). In this blog arti

Time Series Forecasting with Machine Learning Models 2018-09-13T11:38:23+00:00

Neuroevolution – Scaling the Evolution of Artificial Neural Networks

2018-09-06T10:51:46+00:00

In this article we will look at the history of Neuroevolution and present state-of-the-art work that was performed by Google, Uber and other companies.

Neuroevolution describes the evolution of Artificial Neural Networks for problems in the domain of supervised or reinforcement learning. This article is the result of

Neuroevolution – Scaling the Evolution of Artificial Neural Networks 2018-09-06T10:51:46+00:00

Multiplicative LSTM for sequence-based Recommenders

2018-08-21T21:57:45+00:00

Traditional user-item recommenders often neglect the dimension of time, finding for each user a latent representation based on the user’s historical item interactions without any notion of recency and sequence of interactions. Sequence-based recommenders such as Multiplicative LSTMs tackle this issue.

Recommender Systems support the decision making processes of customers with personalized suggestions. They are widely used and influence the daily life of almost ever

Multiplicative LSTM for sequence-based Recommenders 2018-08-21T21:57:45+00:00

Using Power BI Embedded in Apps and Websites

2018-07-23T10:31:01+00:00

Power BI Embedded is a PaaS solution that offers a collection of interfaces to enable the integration of Power BI content into custom apps and websites.

Power BI Embedded is a Platform-as-a-Service (PaaS) solution on Microsoft Azure that offers a collection of interfaces to enable the integration of Power BI content i

Using Power BI Embedded in Apps and Websites 2018-07-23T10:31:01+00:00

Using TensorFlow Models with Core ML on iOS

2018-07-18T11:33:25+00:00

In iOS 11 Apple introduced Core ML, its own framework for Machine Learning on iPhone and iPad. In this article we show how to convert a trained TensorFlow Model and integrate it with an iOS app.

Machine learning and more precisely convolutional neural networks are an interesting approach for image classification on mobile devices. In the recent past it wasn&#

Using TensorFlow Models with Core ML on iOS 2018-07-18T11:33:25+00:00

Use Your TensorFlow Mobile Model in an Android App

2018-06-27T12:21:01+00:00

In this post we'll show how to integrate machine learning, more accurately a neural network, to recognize houseplants in an Android app—using TensorFlow Mobile directly on the device!

Nowadays, modern mobile devices are extremely powerful and enable new approaches. Even if it sounds like a platitude, it is clear that some of these approaches are ve

Use Your TensorFlow Mobile Model in an Android App 2018-06-27T12:21:01+00:00

Generative Adversarial Networks explained

2018-06-26T09:53:19+00:00

Recent improvements on the architecture and the training of Generative Adversarial Networks have rendered them applicable on a greater variety of problems, e.g sequential or discrete data. In this blog article we take a closer look on the general theoretical GAN architecture and its variations.

Neural networks are one of the technologies that have the potential to change our lives forever. Besides lots of applications and machines in the industry they have d

Generative Adversarial Networks explained 2018-06-26T09:53:19+00:00

Text Spotting using semi-supervised Generative Adversarial Networks

2018-06-26T09:57:18+00:00

We built a text spotting (OCR) pipeline that out-performed Google Cloud Vision using semi-supervised Generative Adversarial Networks.

Despite all advances in machine learning due to the advent of deep learning, the latter has one major shortcoming: It requires a lot of data during the learning proce

Text Spotting using semi-supervised Generative Adversarial Networks 2018-06-26T09:57:18+00:00

TensorFlow Mobile: Training and Deploying a Neural Network

2018-09-25T11:10:59+00:00

In this blog series we explain how you can train and deploy a convolutional neural network for image classification to a mobile app using TensorFlow Mobile.

Smart Assistants, fancy image filters in Snapchat and apps like Prisma all have one thing in common—they are powered by Machine Learning. The use of Machine Learning

TensorFlow Mobile: Training and Deploying a Neural Network 2018-09-25T11:10:59+00:00

Managing isolated Environments with PySpark

2018-04-10T13:30:43+00:00

In this article we present a simple solution for managing Isolated Environments with PySpark that we have been using in production for more than a year.

With the sustained success of the Spark data processing platform even data scientists with a strong focus on the Python ecosystem can no longer ignore it. Fortunately

Managing isolated Environments with PySpark 2018-04-10T13:30:43+00:00

Application of Differential Privacy and Randomized Response in Big Data

2018-03-01T09:15:11+00:00

In this blog, I’ll explain some of the basic concepts of differential privacy and talk about how I’ve used it in my Bachelor’s Thesis.

Differential Privacy is a topic of growing interest in the world of Big Data. It is currently being deployed by tech giants like Google and Apple to gain knowledge ab

Application of Differential Privacy and Randomized Response in Big Data 2018-03-01T09:15:11+00:00

Writing a Hive UDF for lookups

2018-02-07T14:42:53+00:00

Let's use a Hive UDF to perform lookups against resources residing in the Hadoop file system (HDFS) which allows non-equi joins.

In today’s blog I am going to take a look at a fairly mundane and unspectacular use of a Hive UDF (user-defined function), that of performing lookups against re

Writing a Hive UDF for lookups 2018-02-07T14:42:53+00:00

Data Science in Production: Packaging, Versioning and Continuous Integration

2018-02-07T14:53:36+00:00

Here's what changes when your data science project grows from a proof of concept. How do you deploy your model, how can updates be rolled out, ...?

A common pattern in most data science projects I participated in is that it’s all fun and games until someone wants to put it into production. From that point in time

Data Science in Production: Packaging, Versioning and Continuous Integration 2018-02-07T14:53:36+00:00

Network Anomaly Detection: Online vs. Offline Machine Learning

2018-09-25T11:06:05+00:00

In this part of our network anomaly detection blogpost series we want to compare two basically different styles of learning.

In this part of our network anomaly detection series we want to compare two basically different styles of learning. The very first post introduced the simple k-means 

Network Anomaly Detection: Online vs. Offline Machine Learning 2018-09-25T11:06:05+00:00
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