2022 and the rise of statworx next

Sebastian Heinz Blog, Data Science

2021 for sure was one of the most exciting, challenging, and rewarding years of my career so far. Like last year, I’ve decided to kickstart this year with a short review of 2021 and give an outlook on what’s on the menu for 2022. Spoiler alert: This year is already casting large shadows for us – due to the rise of statworx next.

Titelbild Recap Digital Festival Zürich 2021

Recap: 5 Highlights from the Digital Festival Zurich 2021

Livia Eichenberger Blog, Data Science

After participating in the Digital Festival for the first time last year, the whole Swiss team of STATWORX was looking forward to this year’s edition, which took place from September 23 to 26 in Zurich at Schiffbau, located conveniently just around the corner of our Swiss office. Under the motto «Make It Personal», a variety of keynotes, labs and networking sessions brought together digital leaders, digital aficionados and innovators, all driven by curiosity, openness and a maker mentality. In keeping with this year’s Digital Festival motto, I would like to share my personal five highlights of this recent event with you now.

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.