Deploy and Scale Machine Learning Models With Kubernetes

Deploy and Scale Machine Learning Models with Kubernetes

Jonas Braun Blog, Data Science

In this article, Jonas Braun reports on the most common way to use Kubernetes: with cloud providers like Google GCP, Amazon AWS or Microsoft Azure. In the article, he looks at how to deploy these containers (i.e. applications or models) reliably and scalably for customers, other applications, internal services or computations with Kubernetes. Finally, the article gives an outlook on tools and further developments.

Titelbild Explainability of Deep Learning Models with Grad-CAM

Car Model Classification III: Explainability of Deep Learning Models with Grad-CAM

Stephan Müller Blog, Data Science

In the third part of our blog series, we cover an essential topic that has gained significant traction in the ML-community in the past years: Explainability. Explainable AI is essential to establish trust in the models we develop. We discuss various approaches for CNN networks, with a particular focus on the Grad-CAM method.

Title 5 Types of Machine Learning Algorithms

5 Types of Machine Learning Algorithms (With Use Cases)

Fran Peric Blog, Data Science

We are encountering Machine Learning algorithms in our daily lives. Some are practical, like Google Translate; others are fun, like Snapchat Filters. Our interaction with artificial intelligence will most likely increase in the next few years. Given the potential impact of Machine Learning models on our future lives, Fran Peric presents to you the five branches of Machine Learning and their key concepts.

Whitepaper: How To Build Trust With Explainable AI

Verena Eikmeier Blog, Data Science

Today, a multitude of methods make it possible to explain even complex AI systems. Even though there are several challenges to be considered, the benefits of XAI in companies are immense. This whitepaper provides an overview of possible applications, advantages, methods, and challenges in employing XAI in companies and thus serves as a guide to this essential future topic.

5 Practical Examples of NLP Use Cases

Felix Plagge Blog, Data Science

Due to recent achievements in deep learning, several different NLP (“Natural Language Processing”) tasks can now be solved with outstanding quality.
In this article, you will learn how NLP applications solve various business problems through five practical examples, which ensured an increase in efficiency and innovation in their field of application.

data engineering

Whitepaper: Machine Learning in the Cloud – Comparing AWS, Azure, and GCP

Alexander Blaufuss Blog, Data Science

In order for companies to continue to be successful in a digital, software and data-driven age, the necessary technical prerequisites must be established. The use of cloud technology is seen as an important element in this process.
In this whitepaper we provide an overview of the range of services offered by the three largest providers for cloud computing, AWS, Azure and GCP.

How To Provide Machine Learning Models With The Help Of Docker Containers

Thomas Alcock Blog, Data Science

More and more companies recognize the potential of artificial intelligence and develop their own ML models. At the same time, these companies are often faced with the challenge of making these models available to users internally and thereby generating added value from the model. In this article, we show what such challenges can be and how Docker enables companies to meet them.