Entries by Mario Meir-Huber

Serverless Analytics

In one of my last posts, I wrote about the fact that Cloud is more PaaS/FaaS then IaaS already. In fact, IaaS doesn’t bring much value at all over traditional architectures. There still are some advantages, but they remain limited. If you want to go for a future-proove archtiecture, Analytics needs to be serverless analytics. […]


Recurrent Neural Network and Long Short-Term Memory

In the last two posts we introduced the core concepts of Deep Learning, Feedforward Neural Network and Convolutional Neural Network. In this post, we will have a look at two other popular deep learning techniques: Recurrent Neural Network and Long Short-Term Memory. Recurrent Neural Network The main difference to the previously introduced Networks is that […]


The Data Science Process

Working with data is a complex thing and not done in some days. It is rather a matter of several sequential steps that lead to a final output. In this post, I present the data science process for project execution in data science. What is the Data Science Process? Data Science is often mainly consisting […]

Cloud IaaS is not the future

About 1,5 years ago I was writing that Cloud is not the future. Instead, I claimed that it is the present. In fact, most companies are already embracing the Cloud. Today, I want to revisit this statement and take it to the next level: Cloud IaaS is not the Future What is wrong about Cloud […]

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 […]