Your project’s codebase keeps growing and it daunts you. We’ve all been there. Maybe this tool can help you. Felix Plagge has written a package that creates a call graph for any Python script. In this article, he first explains what project graphs are useful for and then explains the installation and usage of his package called project_graph.
Do you want to learn Python? Or maybe you need a little reminder from time to time while coding? That’s exactly why cheatsheets were invented! Our first cheatsheet with Python basics is the start of a new blog series, where more cheatsheets will follow in our unique STATWORX style.
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.
Deploying and monitoring machine learning projects is a complex undertaking. In this article, John Vicente presents the typical challenges along the machine learning workflow and describes a possible solution platform with MLflow. In addition, we present three different scenarios that can be used to professionalize machine learning workflows.
Did you want to know how to build a web frontend in Python? In the 4th and last part of our blog series, we develop an interactive game. The user has to guess the car brand and model. Do you think you can beat our AI?
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.
How do we interact with machine learning models in practice? In the second part of our 4-part blog series on car model classification, you will learn how models can be deployed using TensorFlow Serving, and how we can run model queries.
In this 4-part blog series on car model classification, we want to illustrate how an end-to-end deep learning project can be implemented. In the first part, we will show how you can use transfer learning to tackle car image classification. In particular, you will learn how a pre-trained ResNet model can be fine-tuned to tackle a downstream task.
“Building trust through human-centric AI”: this is the slogan under which the European Commission presented its proposal for regulating Artificial Intelligence (AI regulation) in April 2021. In this commentary, Oliver Guggenbühl examines the EU’s proposal from various angles, highlighting both positive aspects and shortcomings.
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.