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
Von Eli Mirvic| 2018-11-07T10:27:38+00:00 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
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
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
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
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
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
With Kotlin gaining more and more popularity (especially among Android developers), a new option for dependency injection has risen: Koin. Here are 5 reasons why you should rely on Koin in your new Android Projects!
For many years now, there was basically only one dependency injection library used in Android app projects: Dagger (2). While Dagger is offering all the features you
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