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Home 2018-09-14T14:37:50+00:00

Remote Work: 12 Guidelines for a Successful Remote Team

Von | 05. Dezember 2018|

Two years ago when someone mentioned remote work to me, the first picture which came into my mind was the surfing colleague on an exotic island not available because of the lack of broadband connection. My perception was, remote work can not be productive nor can it be efficient or fun. Oh how wrong I was ...

Two years ago when someone mentioned remote work to me, the first picture which came into my mind was the surfing colleague on an exotic island not available because

4 Ways to Manage Your OpenStack Secrets with Terraform and git

Von | 29. November 2018|

Terraform and OpenStack provide some clever ways of authenticating to OpenStack and configuring your clouds. This article shows you four easy ways so you never have to worry about accidentally uploading secrets to places where they shouldn't be.

Uploading secrets (i.e. passwords and usernames) to version control is an obviously terrible idea. Yet, there are almost 450,000 commits to github for the search term

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

Von | 26. November 2018|

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

Working efficiently with Jupyter Notebooks

Von | 20. November 2018|

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

AWS ECS: Kickstart Containers into Production

Von | 12. November 2018|

There are quite some ways to bring containers into production, e.g. Kubernetes, Openshift or Docker Swarm. This article will present another viable addition to this list: Elastic Container Service on AWS (AWS ECS) as solution to run containers at scale.

There are quite some ways to bring containers into production, e.g. Kubernetes, Openshift or Docker Swarm. This article will present another viable addition to this l

Rethinking Modern Data Warehouse with Azure Analysis Services

Von | 07. November 2018|

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

Development of an Automated Scraper to Identify Trends in Web Development

Von | 31. Oktober 2018|

As part of my Bachelor's Thesis I implemented a scraper which collects information about websites' HTML, CSS, and JavaScript and checks for Lighthouse KPI. Here's how it works.

TL;DR As part of my Bachelor’s Thesis I implemented a scraper which collects information about websites‘ HTML, CSS, and JavaScript. Furthermore, the Light

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

Von | 29. Oktober 2018|

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

Neuroevolution: A Primer On Evolving Artificial Neural Networks

Von | 24. Oktober 2018|

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

How to Manage Machine Learning Models

Von | 22. Oktober 2018|

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

When Pepper met Android

Von | 19. Oktober 2018|

What's new with Pepper? It looks just the same. True. But you know, it's the inside that counts! As announced at the Google I/O 2016, and finally released end of June this year, Android is now powering Pepper, changing the way pepper is programmed in every way. Here are the changes.

Since its commercial launch about two years ago, Pepper the Robot has conquered so diverse spaces around the globe that a day passing by without Pepper in the press i

Findings in Running Google Dataproc

Von | 17. Oktober 2018|

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

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