Since Open AI’s DALL·E 2 and Google’s Imagen, AI art and generally creating images with machine learning models got immense attention. Basically, everyone wants to create AI Images. And with the tools, available anyone can do it. This article is a get-started guide on how you can create your own…

A simple and scalable approach with custom container — I regularly guide customers on how they can scale their training on the Cloud. While every customer has unique requirements and expectations, the overall process is the same. In this article, we train a transformer model (DistilBert) with Vertex AI. As a machine learning Framework, I use TensorFlow. It will…

Reducing your ML inference costs can be so easy. 4 steps I regularly recommend to companies. — In this article, I cover the most common ways of saving costs. Some of them are just applying common sense, some are a nice way to utilize another service, while others require more technical knowledge. Cloud Run instead of Vertex AI Endpoints One of the major disadvantages of Vertex AI Endpoints is the fact that there is…

A personal opinion based on workshops with many different customers — Back in the days, at a time when ML was not all over the place. Companies already did a lot of data processing for other purposes. A large number of Cloud Composer workflows got created. Nowadays companies often try to implement machine learning workflows into their existing Cloud Composer workflows…

How to interpret and understand ML models with explainable AI (XAI) — Explainable AI is as the name already suggests it’s all about explaining predictions our models are making. It helps you to understand why your machine learning model is making certain decisions. Useful to prove if the model is focusing on the right patterns or to find issues like bias or…

The good and the bad stuff, no seriously just a deep dive into the product — Putting a similarity index into production at scale is a pretty hard challenge. It requires a whole bunch of infrastructure working closely together. You need to handle a large amount of data at low latency. …

We can use machine learning to return meaningful similar images, text, or audio in the proper context. Simple, and fast. — Imagine the programmatic effort needed to implement an algorithm to visually compare different T-Shirts to find matching ones. Archiving good results with a traditional programmatic approach is challenging. With deep learning, we can easily scale this to an unlimited number of different use cases. We do this by using the…

Automatically deploy and run your machine learning pipeline. A real deep dive, step by step. — In this article, we cover how Vertex AI Pipelines are scheduled and gather a deep understanding of the inner workings. We use that knowledge to implement an automatic deployment for our machine learning pipeline. At the end of this article, you have a Vertex AI Pipeline that is updated automatically…

The future is quantum computing — IonQ recently went public and is now the first publicly traded company that solely focuses on quantum computing. This article is an introduction on how to access and use IonQs quantum computers using Google Cirq. This article does not cover the basics of quantum circuits. Let me know if you’re…