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

Managing isolated Environments with PySpark

2018-04-10T13:30:43+00:00

In this article we present a simple solution for managing Isolated Environments with PySpark that we have been using in production for more than a year.

With the sustained success of the Spark data processing platform even data scientists with a strong focus on the Python ecosystem can no longer ignore it. Fortunately

Managing isolated Environments with PySpark 2018-04-10T13:30:43+00:00

Data Science in Production: Packaging, Versioning and Continuous Integration

2018-02-07T14:53:36+00:00

Here's what changes when your data science project grows from a proof of concept. How do you deploy your model, how can updates be rolled out, ...?

A common pattern in most data science projects I participated in is that it’s all fun and games until someone wants to put it into production. From that point in time

Data Science in Production: Packaging, Versioning and Continuous Integration 2018-02-07T14:53:36+00:00

Real-time detection of anomalies in computer networks with methods of machine learning: Stop the (data)-thief!

2017-11-27T15:18:05+00:00

This blog post describes some basic concepts and shows a prototypical architecture for network anomaly detection in real-time.

This blog post shows some results and concepts of a master’s thesis here at inovex. It describes some basic concepts and shows a prototypical architecture for detecti

Real-time detection of anomalies in computer networks with methods of machine learning: Stop the (data)-thief! 2017-11-27T15:18:05+00:00

Causal Inference and Propensity Score Methods

2017-11-27T15:30:21+00:00

In supervised learning, correlation is crucial to predict the target variable with the help of the feature variables. But what good is causation?

In the field of machine learning and particularly in supervised learning, correlation is crucial to predict the target variable with the help of the feature variables

Causal Inference and Propensity Score Methods 2017-11-27T15:30:21+00:00

Hive UDFs and UDAFs with Python

2017-11-27T15:30:25+00:00

In this post we focus on how to write sophisticated User Defined (Aggregated) Functions (UD(A)Fs) for Apache Hive in Python.

Sometimes the analytical power of built-in Hive functions is just not enough. In this case it is possible to write hand-tailored User-Defined Functions (UDFs) for tra

Hive UDFs and UDAFs with Python 2017-11-27T15:30:25+00:00

HyperLogLog on Spark Streaming – Schätzung von Kardinalitäten innerhalb eines Datenstroms

2017-11-29T10:10:27+00:00

Untersuchung der Implementierung und Praxistauglichkeit von HyperLogLog auf Apache Spark Streaming mithilfe eines einfachen Prototyps.

Im Rahmen eines Research-Projektes wurde die Implementierung und Praxistauglichkeit von HyperLogLog auf Apache Spark Streaming mithilfe eines einfachen Prototyps unte

HyperLogLog on Spark Streaming – Schätzung von Kardinalitäten innerhalb eines Datenstroms 2017-11-29T10:10:27+00:00

Apache Mesos: Build your own Framework

2017-11-29T13:59:55+00:00

In this final blog post of our 3 part series we will have a look at how you can build your own Apache Mesos framework.

In this final blog post of our 3 part series we will have a look at how you can build your own Apache Mesos framework. If you’re new to Mesos have a look at our

Apache Mesos: Build your own Framework 2017-11-29T13:59:55+00:00

Cassandra Test Lab [Tutorial]

2018-06-14T15:42:12+00:00

We present a simple way to create your Cassandra cluster and experiment with data modeling, different configurations, cluster sizes, topologies etc.

Apache Cassandra is a really impressive piece of technology. When it comes to extreme performance requirements, it is definitely a solution one should look into. Yet

Cassandra Test Lab [Tutorial] 2018-06-14T15:42:12+00:00

Apache Mesos: Marathon

2017-11-29T14:29:22+00:00

We want to show how to run tasks/applications on your Mesos cluster with Marathon, an init-system for Mesos built and maintained by Mesosphere. 

In the previous blog post we described the basics and components of Mesos. Now we want to show you how to run tasks/applications on your Mesos cluster with Marathon,

Apache Mesos: Marathon 2017-11-29T14:29:22+00:00
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