Learn how to structure your data with knowledge graphs in this introduction blogpost to knowledge graphs and its business applications.

An easy way of serving a model in Vertex AI is by deploying a TensorFlow SavedModel using the pre-built containers. However, the lack of an official TensorFlow implementation for YOLO prevents us from directly using this methodology. In this blogpost, our ML Engineer Jérémy explains step by step one of the possible solutions for tackling this problem, which involves converting your Darknet weights to a TensorFlow SavedModel.

We had the honour to welcome Niels Rogge, ML Engineer at Huggingface. He shared with us how he started contributing to the Huggingface transformers repo, the lessons he learned along the way, and how anyone can start making contributions in various ways.

In this blogpost we’ll have a look at Anthos, a platform created by Google to easily manage multi and hybrid-cloud environments running Kubernetes clusters.

We are often asked how to detect very small objects in high resolution, i.e. very large images. A good example is finding objects in aerial images. The goal of this blog post is to demonstrate a practical approach to this problem using Slicing Aided Hyper Inference (SAHI).

From the ML6 community to the ML6 Maffia, our alumni network. How our mutual passion for technology, connects us for life.

In this blogpost, we explain our multi-article summarization approach, where we try to partially eliminate the black-box and make the summarization process more accessible to the user.Our summarization pipeline relies on the idea that sentences are the basic building blocks of any article.

In this blogpost, our ML engineer Juta will dive into the world of multimodal AI. Multimodal AI is able to reason about different modalities, such as text and images, at the same time and leads to more general understanding across domains.

Data Engineer Liam takes a look at how Pipelines can be deployed on the new and improved GCP product, Vertex AI Pipelines. The recently released tool takes a more managed approach to Pipelines in GCP, which promises to make running MLOps pipelines in the cloud easier than ever.

What is exactly that DNA that not only differentiates ML6 but also defines us?We thought it was time to explain this concept using examples and thus asked our HR Business Partner, Kaat van Doren, to give you some more insight into this.

Global warming is one of the biggest problems humanity is currently facing. Despite governmental efforts to reduce the output of greenhouse gasses, the emissions still rise every day. More and more, technology seems to be a promising tool to battle this issue. That is why we at ML6, decided to dive deep into some real-life use cases from four different domains where machine learning can be applied to help the planet.

The goal of this project was to increase the efficiency in notary offices by digitizing the wealth of knowledge inside their archives. These archives contain both text-layered documents as well as numerous scans of paperwork.

In our last blogpost, we have talked about what Trustworthy and Ethical AI is, and how we at ML6 help our clients build trust with their customers and employees through our Ethical AI Risk Assessment. In this post, we want to make it more practical, and demonstrate which dimensions and questions we might consider during such an assessment.

ML6 intern, Amir, studied ways in which to implement a drone that can perform follow-me behavior. The approach that he decided to investigate, is the possibility of using Reinforcement Learning (RL) as a means to allow an agent to train itself to perform the desired behavior.

Decision Forests are very powerful tools to be used as they are simple algorithms, and easy to understand. Recently, TensorFlow made a huge step by incorporating these models into their code stack. Machine learning expert, Pieter Coussement, shares in his latest blogpost how the TensorFlow Serving code was modified to accept TensorFlow Decision Forest models, and how to use this code as a custom docker image to serve the TF-DF models in three easy steps.

Our Machine Learning engineer, Jeremy, came across an interesting flaw that occurs when using Google Kubernetes Engine in combination with Terraform. Interested in finding out the flaw that Jeremy encountered and how we ended up fixing it? Read it in his latest Medium blogpost and learn how default settings can save money and trees.

We often get the question to forecast sales demand for customers. Recently we’ve done this successfully using Prophet. A new package called NeuralProphet builds upon this: it adds some neural networks to the mix. In this blog post we compare both time series forecasting models on a real-world dataset. We're predicting B2C sales volumes in retail to find out both methods' strengths and weaknesses.

To prove our dedication towards information security, our security program is certified against the ISO/IEC 27001:2017 standard. This internationally accepted standard provides requirements for establishing, implementing, maintaining and continually improving information security within an organization. ‍In this blogpost, we explain the why, what and how behind our ISO 27001 certification.

We take a look at how models can be deployed and used for prediction on the new and improved GCP product, Vertex AI. The platform was launched a couple of weeks ago and aside from adding additional features, existing features such as serving models for predictions have been updated and improved. We take a look at how endpoints are now a central part of model predictions and how containers have made deploying custom models very straightforward.

We explore the effectiveness of recently released face anonymization algorithms

March - a proptech player in commercial real estate - uses AI to disrupt the traditional ways of working in the commercial real estate market. The March Matching Engine uses a wealth of data to generate high-impact leads, allowing March’s real estate professionals to focus most of their time on the customer.