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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.

Finally! Bayesian Hierarchical Modelling at Scale

2021-03-03T11:13:43+00:00

In this blog post, I want to draw your attention to the somewhat dusty Bayesian Hierarchical Modelling. Modern techniques and frameworks allow you to finally apply this cool method on datasets with sizes much bigger than what was possible before and thus letting it really shine.

Introduction Since the advent of deep learning, everything is or has to be about Artificial Intelligence, so it seems. Even software which is applying traditional tec

Finally! Bayesian Hierarchical Modelling at Scale2021-03-03T11:13:43+00:00

Data Processing Scaled Up and Out with Dask and RAPIDS: Installing a Data Science App as Dask Client (2/3)

2021-03-02T11:55:07+00:00

In this part we will add containerized applications to our Kubernetes cluster to be able to run data processing workloads in our cluster with Dask. Being more precise: we will prepare a notebook image that has CUDA installed which is required if we want to use GPU-based frameworks.

This blog post tutorial shows how a scalable and high-performance environment for machine learning can be set up using the ingredients GPUs, Kubernetes clusters, Dask

Data Processing Scaled Up and Out with Dask and RAPIDS: Installing a Data Science App as Dask Client (2/3)2021-03-02T11:55:07+00:00

Improving Image Retrieval with User Feedback

2021-02-08T12:05:31+00:00

A problem occurs when an image retrieval method delivers irrelevant results. This post shows how user interaction can be utilized to overcome this problem.

Content based image retrieval is a field in computer vision. The aim is to find the most similar images to a given input image, where the similarity refers to the sem

Improving Image Retrieval with User Feedback2021-02-08T12:05:31+00:00

Der Tag eines Data Engineers bei inovex – Dirigieren eines Datenorchesters

2021-01-28T10:22:39+00:00

Der Tag eines Data Engineers kann vielfältig sein: Datenaufbereitung und -analyse, die Konzeption von KI-Modellen etc. Im Blog-Artikel beschreibt Simon Kufeld, wie ein Tag als Senior Data Engineer bei inovex ablaufen kann.

Der Tag eines Data Engineers kann vielfältig sein: Datenaufbereitung und -analyse, die Konzeption von KI-Modellen etc. Bei inovex bleiben die Möglichkeiten, sich im U

Der Tag eines Data Engineers bei inovex – Dirigieren eines Datenorchesters2021-01-28T10:22:39+00:00

One Shot Learning: Eure Fragen beantwortet

2020-12-23T12:45:44+00:00

Bei unserem Meetup zum Thema One Shot Learning blieben einige Fragen unbeantwortet. Mai und Sebastian haben sich dieser angenommen und an dieser Stelle beantwortet. Hast du noch weitere Fragen? Dann stelle sie unseren Expert:innen gerne in den Kommentaren!

Bei unserem Meetup zum Thema One Shot Learning blieben einige Fragen unbeantwortet. Mai und Sebastian haben sich dieser angenommen und an dieser Stelle beantwortet. H

One Shot Learning: Eure Fragen beantwortet2020-12-23T12:45:44+00:00

Der Tag eines Data Scientist bei inovex – Mehr als nur Daten

2020-12-22T16:08:23+00:00

Der Tag eines Data Scientist kann vielfältig sein: Datenaufbereitung und -analyse, die Konzeption von KI-Modellen und viel mehr. Bei inovex sind die Möglichkeiten, sich einzubringen, nicht auf den eigenen Fachbereich beschränkt. Hier beschreibe ich, wie ein Tag als Senior Data Scientist bei inovex ablaufen kann.

Der Tag eines Data Scientist kann vielfältig sein: Datenaufbereitung und -analyse, die Konzeption von KI-Modellen und viel mehr. Bei inovex bleiben die Möglichkeiten,

Der Tag eines Data Scientist bei inovex – Mehr als nur Daten2020-12-22T16:08:23+00:00

Deep Learning for Mobile Devices with TensorFlow Lite: Train Your Custom Object Detector

2021-03-05T16:07:30+00:00

In the second article of our blog post series about TensorFlow Mobile we are working on quantization-aware model training with the TensorFlow Object Detection API. In the hands-on example we build and train a quantization-aware object detector for cars.

This is the second article of our blog post series about TensorFlow Mo

Deep Learning for Mobile Devices with TensorFlow Lite: Train Your Custom Object Detector2021-03-05T16:07:30+00:00

A Close Look at the Workings of Apache Druid

2020-11-30T18:08:29+00:00

Apache Druid is a real-time analytics database that bridges the possibility of persisting large amounts of data with that of being able to extract information from it without having to wait unreasonable amounts of time. Read this article for operational insights and tips on how to get started.

Apache Druid is a real-time analytics database that bridges the possibility of persisting large amounts of data with that of being able to extract information from it

A Close Look at the Workings of Apache Druid2020-11-30T18:08:29+00:00

Inverse Reinforcement Learning and Finding Proper Reward Signals for Snake-like Robots

2020-11-26T11:04:25+00:00

Learning is a process that can be observed across all living creatures – and also machines with the advent of sophisticated hardware and algorithms. This article introduces preference-based inverse reinforcement learning and explains how it can support a snake-like robot to learn to move forward efficiently.

Humans are constantly being taught and acquire knowledge: first by parents, later in school by teachers and at work by colleagues. In fact, learning is a process that

Inverse Reinforcement Learning and Finding Proper Reward Signals for Snake-like Robots2020-11-26T11:04:25+00:00

Modelling the Time-of-Arrival Using Distributions

2020-11-25T17:54:44+00:00

Estimating the time-of-arrival is a common Problem in many Scenarios. This post will show a Distribution-based approach that enables us to get more information about our time-of-arrival and how we could use this information for decision making in the logistics related industry.

Estimating the time-of-arrival is a common problem in a wide range of settings, e.g. in logistics. This post will show a distribution-based approach that enables us t

Modelling the Time-of-Arrival Using Distributions2020-11-25T17:54:44+00:00

Deep Learning for Mobile Devices with TensorFlow Lite: Concepts and Architectures

2020-11-13T10:19:37+00:00

This first post tackles some of the theoretical background of on-device machine learning, including quantization and state-of-the-art model architectures for TensorFlow Lite.

The amount of mobile applications making use of some sort of machine learning is quickly increasing, just as the number of potential use cases in this area. Whenever

Deep Learning for Mobile Devices with TensorFlow Lite: Concepts and Architectures2020-11-13T10:19:37+00:00

Docker as Remote Interpreter for PyCharm Professional

2020-10-29T13:14:11+00:00

Wouldn't it be nice if we could mimic the productive cloud environment on our local machine to speed up development and simplify debugging? This post explains how to set up PyCharm Professional to use a local Docker container as a remote interpreter that mirrors the behavior of your production environment.

Wouldn’t it be nice if we could mimic the productive cloud environment on our local machine to speed up development and simplify debugging? This post explains h

Docker as Remote Interpreter for PyCharm Professional2020-10-29T13:14:11+00:00

Deep Learning on Bad Time Series Data: Corrupt, Sparse, Irregular and Ugly

2020-10-22T09:53:03+00:00

How do you train neural networks on time series that are non-uniformly sampled,  irregularly sampled, have non-equidistant timesteps, or have missing or corrupt values? In the following post, I try to summarize and point to effective methods for dealing with such data.

How do you train neural networks on time series that are non-uniformly sampled, irregularly sampled, have non-equidistant timesteps, or have missing or corrupt values

Deep Learning on Bad Time Series Data: Corrupt, Sparse, Irregular and Ugly2020-10-22T09:53:03+00:00

Hybrid Methods for Time Series Forecasting

2021-02-10T09:17:10+00:00

Hybrid time series forecasting methods promise to advance time series forecasting by combining the best aspects of statistics and machine learning. This blog post gives a deeper understanding of the different approaches to forecasting and seeks to give hints on choosing an appropriate algorithm.

Time series forecasting is a crucial task in various fields of business and science. There are two co-existing approaches to time series forecasting, statistical meth

Hybrid Methods for Time Series Forecasting2021-02-10T09:17:10+00:00
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