Entries by Mario Meir-Huber

Data abstraction: the what and the why

Large enterprises have a lot of legacy systems in their footprint. This created a lot of challenges (but also opportunities!) for system integrators. Now, since companies strive to become data driven, it becomes an even bigger challenge. But luckily there is a new thing out there that can help: data abstraction. Data Abstraction: why do […]


Introduction to Spark ML

Spark ML is Apache Spark’s answer to machine learning and data science. The library has several powerful features for typical machine learning and data science tasks. In the following posts I will introduce Spark ML. What is Spark ML? The goals of MLlib is to solve complex Machine Learning and Data Science tasks in an […]

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Convolutional Neural Network (CNN) and Feedforward Neural Network

In the last couple of posts, we’ve learned about various aspects of Machine Learning. Now, we will focus on other aspects of Machine Learning: Deep Learning. After introducing the key concepts of Deep Learning in the previous post, we will have a look at two concepts: the Convolutional Neural Network (CNN) and the Feedforward Neural […]

AI Ethics: towards a sustainable AI and Data business

AI and Ethics is a complex and ofthen discussed topic at different conferences, usergroups and forums. It even got picked up by the European commission. I would argue that it should actually go one step further: it should be part of every corporate responsibility strategy – just like social and environmental elements. AI Ethics: what […]


Important data sources you need

For Data itself, there are a lot of different sources that are needed. Based on the company and industry, they differ a lot. However, to create a complex view on your company, it isn’t necessary only to have your own data. There are several other data sources you should consider. Data you already have The […]

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Classification algorithms: Random Forest and Naive Bayes

In the first posts, I introduced different type of Machine Learning concepts. On of them is classification. Basically, classification is about identifying to which set of categories a certain observation belongs. Classifications are normally of supervised learning techniques. A typical classification is Spam detection in e-mails – the two possible classifications in this case are […]