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.

MLaaS: Maschinelles Lernen in der Cloud

2018-12-13T15:22:33+00:00

Machine learning as a service (MLaaS) bietet Unternehmen eine einfache Möglichkeit, Daten zu verarbeiten, Modelle zu trainieren und Prognosen zu erstellen. In diesem Artikel werden die Angebote von vier der größten Cloud-Anbieter vorgestellt: GCP, AWS, MS Azure und IBM Cloud/Watson.

Cloud Computing gewinnt durch sein flexibles Bereitstellungsmodell immer größere Bedeutung. Von Software (SaaS),Plattformen (PaaS) bis hin zur IT-Infrastruktur (IaaS)

MLaaS: Maschinelles Lernen in der Cloud 2018-12-13T15:22:33+00:00

Traditionelles vs. virtuelles Data Warehouse: Vergleich der ETL-Performance

2018-11-26T10:06:11+00:00

Durch die Virtualisierung von ETL-Prozessen kann eine DWH-Architektur an Flexibilität gewinnen, allerdings resultiert daraus eine reduzierte Performanz. Hier sind die Ergebnisse meiner Masterarbeit, in der ich diesen Trade-off eines virtuellen Data Warehouse untersucht habe.

Durch die Virtualisierung von ETL-Prozessen kann eine Data-Warehouse-Architektur an Flexibilität gewinnen, der daraus resultierende Nachteil ist eine reduzierte Perfo

Traditionelles vs. virtuelles Data Warehouse: Vergleich der ETL-Performance 2018-11-26T10:06:11+00:00

Working efficiently with Jupyter Notebooks

2018-11-20T11:31:51+00:00

Being in the data science domain for quite some years, I have seen good Jupyter notebooks but also a lot of ugly ones. Follow these best practices to to work more efficiently with your notebooks and strike the perfect balance between text, code and visualisations.

If you have ever done something analytical or anything closely related to data science in Python, there is just no way you have not heard of or IPython or Jupyter not

Working efficiently with Jupyter Notebooks 2018-11-20T11:31:51+00:00

Rethinking Modern Data Warehouse with Azure Analysis Services

2018-11-07T10:27:38+00:00

Azure Analysis Services is able to consume data from a variety of sources including storages like Azure Blob Storage or Azure Data Lake Store. Here's how you lift our file-based data directly to Azure Analysis Services.

Before I got more familiar with Microsoft Azure and all its PaaS components such as Azure Analysis Services, I was routinely sticking to Microsoft’s on-premises BI st

Rethinking Modern Data Warehouse with Azure Analysis Services 2018-11-07T10:27:38+00:00

From Exploration to Production—Bridging the Deployment Gap for Deep Learning (Part 2)

2018-10-29T09:56:47+00:00

In this blogposts on deep learning model exploration, translation, and deployment we expand on the previous article with two additional approaches for model deployment: TensorFlow Serving and Docker as well as a rather hobbyist approach in which we build a simple web application that serves our model.

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

From Exploration to Production—Bridging the Deployment Gap for Deep Learning (Part 2) 2018-10-29T09:56:47+00:00

Neuroevolution: A Primer On Evolving Artificial Neural Networks

2018-10-25T09:28:13+00:00

In the last years, artificial neural networks (ANN) have successfully been applied across a number of tasks. However, designing well performing ANNs requires expert knowledge and experience. Neuroevolution aims at solving this difficult and often time-consuming process by using evolutionary techniques.

In the last years, artificial neural networks (ANN) have successfully been applied across a number of tasks, such as image classification, speech recognition and natu

Neuroevolution: A Primer On Evolving Artificial Neural Networks 2018-10-25T09:28:13+00:00

How to Manage Machine Learning Models

2018-12-06T09:53:24+00:00

In the past few moths a slew of Machine Learning management platforms arose. In this article we have a look at ModelDB which supports data scientists by keeping track of models, datasources and parameters. If you use scikit-learn or SparkML it promises easy integration and offers additional visualisation tools.

Developing a good machine learning model is not straight forward, but rather an iterative process which involves many steps. Mostly Data Scientists start by building

How to Manage Machine Learning Models 2018-12-06T09:53:24+00:00

Findings in Running Google Dataproc

2018-11-15T13:59:50+00:00

In this article we will share the experience we have gained from running Dataproc clusters on Google Cloud. We specifically selected topics which you definitively have to deal with if you want to operate Dataproc clusters in production and that differ from practices we are used to from on-premises clusters.

In this article we will share the experience we have gained from running Dataproc clusters on Google Cloud. We specifically selected topics which you definitively hav

Findings in Running Google Dataproc 2018-11-15T13:59:50+00:00

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

2018-10-22T14:13:33+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 2018-10-22T14:13:33+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
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