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.

Machine Learning Interpretability: Do You Know What Your Model Is Doing?

2019-02-16T19:18:45+00:00

Unlike usual performance metrics, fairness, safety and transparency in machine learning models are much harder if not impossible to quantify. Here are some techniques (and examples) to provide interpretability, to make decision systems understandable not only for their creators, but also for their customers and users.

Machine learning has a great potential to improve data products and business processes. It is used to propose products and news articles that we might be interested i

Machine Learning Interpretability: Do You Know What Your Model Is Doing? 2019-02-16T19:18:45+00:00

SeqPolicyNet: Querying Elasticsearch by Asking Questions about Movies

2019-01-30T09:51:46+00:00

This article presents SeqPolicyNet, our Deep Learning approach to accessing information stored in an Elasticsearch instance given natural language questions.

tl;dr (spoiler alert): We’ve trained an advanced neural network to query Elasticsearch based on natural language questions. Our model, called SeqPolicyNet, incorporat

SeqPolicyNet: Querying Elasticsearch by Asking Questions about Movies 2019-01-30T09:51:46+00:00

Hybride DWH-Architekturen: Mehrwerte von Cloud Services (Teil 3)

2019-01-11T17:26:38+00:00

Eignen sich hybride DWH-Architekturen für Szenarien mit global verteilten Daten? Wie wird die Ausfallsicherheit in der Cloud sichergestellt und welche Herausforderungen und Risiken ergeben sich? Unser Fazit zu hybriden DWH-Architekturen.

Kürzlich wurde das Buch BI & Analytics in der Cloud im dpunkt Verlag veröffentlicht, in dem von verschiedenen Fachautoren des TDWI die Besonderheiten zu Cloud Bus

Hybride DWH-Architekturen: Mehrwerte von Cloud Services (Teil 3) 2019-01-11T17:26:38+00:00

Hybride DWH-Architekturen: Mehrwerte von Cloud Services (Teil 2)

2019-01-16T09:01:44+00:00

Wie lassen sich hybride Technologien im Data-Warehouse kombinieren? Wie tragen erhöhte Agilität und schnelle Innovationszyklen der Hersteller zur Optimierung von Betriebskosten im Cloud-Kontext bei? Teil 2 unserer dreiteiligen Artikelserie über hybride DWH-Architekturen.

Kürzlich wurde das Buch BI & Analytics in der Cloud im dpunkt-Verlag veröffentlicht, in dem von verschiedenen Fachautoren des TDWI die Besonderheiten zu Cloud Bus

Hybride DWH-Architekturen: Mehrwerte von Cloud Services (Teil 2) 2019-01-16T09:01:44+00:00

Hybride DWH-Architekturen: Mehrwerte von Cloud Services (Teil 1)

2019-01-16T09:01:39+00:00

Wie passen Cloud und Data-Warehousing zusammen, wie wird Connectivity in die Cloud hergestellt und welche Skalierungsmöglichkeiten und Chancen ergeben sich dadurch? Teil 1 unserer dreiteiligen Artikelserie über hybride DWH-Architekturen.

Kürzlich wurde das Buch BI & Analytics in der Cloud im dpunkt-Verlag veröffentlicht, in dem von verschiedenen Fachautoren des TDWI die Besonderheiten zu Cloud Bus

Hybride DWH-Architekturen: Mehrwerte von Cloud Services (Teil 1) 2019-01-16T09:01:39+00:00

Grafana Loki: Scalable and Flexible Logfile Management

2019-01-22T07:27:51+00:00

Loki is a logfile aggregator that collects log streams. It does so by storing log streams as well as labels attached to them. Loki works like Prometheus, but for logs. Each log stream is indexed and its occurrence is tracked via a timestamp.

Right now there are three popular platforms to build a scalable and flexibel logfile management solution on-premise: splunk, elastic stack and graylog. Most customers

Grafana Loki: Scalable and Flexible Logfile Management 2019-01-22T07:27:51+00:00

Deep Learning Fundamentals

2019-01-07T15:57:13+00:00

This article unveils the connections between artificial intelligence, machine learning and deep learning based on a simple example. It suits as an introduction for newbies as well as a reference point for advanced readers looking for more complex content.

There has always been a gap between the capabilities of men and machine. While computers were able to perform complex multiplications or store large amounts of data,

Deep Learning Fundamentals 2019-01-07T15:57:13+00:00

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

2018-12-18T17:06:14+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 2018-12-18T17:06:14+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
Mehr Beiträge laden