inovex Blog

A Close Look at the Workings of Apache Druid

Von Gabriel-Mihai Ruiu|2020-11-30T18:08:29+00:0001. Dezember 2020|

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

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

Von Andreas Binder|2020-11-26T11:04:25+00:0026. November 2020|

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

Building a GraphQL Example Application with Golang [Tutorial]

Von Gabriel-Mihai Ruiu|2020-11-24T15:42:50+00:0024. November 2020|

This article teaches you the solid basics of implementing a GraphQL interface using golang and the 99designs/gqlgen library.

Most of the people reading this are probably familiar with using and implementing REST interfaces to manage data exchange between automated processes. It is a tried a

Modelling the Time-of-Arrival Using Distributions

Von Jonas Laake|2020-11-25T17:54:44+00:0019. November 2020|

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

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

Von Robin Baumann|2020-11-13T10:19:37+00:0013. November 2020|

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

Grafana Annotations with Prometheus (a Deep Dive)

Von Björn Fischer|2020-11-05T14:21:00+00:0005. November 2020|

Grafana is often used in conjunction with Prometheus to visualize time series and compose dashboards for monitoring purposes. In this blog article, I will dive deep into the specifics of Grafana annotations for data that does not fit into time series graphs – and how to use them with Prometheus as a data source.

Grafana is often used in conjunction with Prometheus to visualize time series and compose dashboards for monitoring purposes. A variety of data fits really well into

Journey Into the World of Remote Coaching

Von Stefan Pollklas|2020-11-03T16:03:23+00:0003. November 2020|

We have learned in the past months that it is indeed possible to deliver agile coaching to our customers on a 100% remote basis and we would like to share some insights and learnings about how to manage a remote coaching project kick-off successfully.

By now, you may have heard the same pandemic tune over and over again: The emergence and spread of SARS-CoV-2 has shaken the ways of working we had taken for granted.

How Does a Remote Virtual Training Work?

Von Collin Rogowski|2020-11-26T13:34:14+00:0026. Oktober 2020|

Since the start of the pandemic, we have begun offering our trainings virtually. As a virtual training experience is quite different from a classroom training, I'll give you an overview of how a virtual training works and some of the (design) choices we made.

Since the start of the pandemic, we have begun offering our trainings virtually. As a virtual training experience is quite different from the experience a classroom t

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

Von Darjan Salaj|2020-10-22T09:53:03+00:0021. Oktober 2020|

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

Hybrid Methods for Time Series Forecasting

Von Daniel Berberich|2020-10-19T10:59:52+00:0019. Oktober 2020|

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

Federated Learning: Frameworks for Decentralized Private Training – Part 2

Von Christian Becker|2020-10-16T12:31:12+00:0014. Oktober 2020|

This blogpost evaluates three different Federated Learning frameworks and the concepts they use to achieve a collaborative training. With Federated Learning, numerous previously unusable sensitive data sources now can be used for collaborative Machine Learning.

This blog post evaluates three different Federated Learning frameworks and

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