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.

Sustainability is a trending topic because companies are focussing, besides economic parameters, on a number of environmental, social, and governance (ESG) criteria. Depending on your industry, the energy used to power your IT workload can be a major contributor to your environmental footprint.

In this blogpost, we take a look at a promising new piece of hardware: the openCV AI Kit. An AI capable edge camera device with a sturdy build and solid 4K 12MP image and video quality. Exploring just how easy it can be to deploy a custom model on it, by showing you how to do just that. We look at the code and setup required to deploy a background segmentation model onto the device. Turning it into a webcam with background replacement built in, to give an example of the workflow.

Google just launched Vertex AI and our own ML in production experts are excited. We're showing you the story of GCP AI tooling and why Google decided to release Vertex now and where it's coming from. All of Google Cloud's AI and ML tools are now unified under a single banner and we saw some brand new products pop up as well. We're summarising the new Vertex platform tools and will follow up with more in-depth blogposts in the coming weeks. Check out what this means for your business, whether you're already GCP customer, interested to migrate to the new stack or just an enthusiast. This update is a big one, and we hope you'll follow us in exploring this new bastion of MLOps that Google Cloud has supposedly created!

In this blogpost, our Head of Labs, Jens Bontinck, outlines the benefits of multi-cloud, looks at the risks and complexity and shares his top three tips that make multi- cloud feasible.

In this blogpost we’ll set up a docker container to run an NVIDIA deepstream pipeline on a GPU or a Jetson and show you some tricks to get the most out of it! Deepstream’s documentation, guides and sample projects are few and far between, so this article aims to be a reference to get you from Deepstream zero to Deepstream-hey-I-can-make -a-pipeline-now and beyond!

In our latest interview, we discuss Ethical AI and why you should care about ethics. Our Chief Strategy Officer and AI Expert discuss some of the major challenges and how we tackle these at ML6.

In this blogpost, we outline four major technology trends we notice the AI R&D communities and industry are actively working towards sustainability in AI. We also share ML6’s key principles for AI development as we believe that implementing AI can be done sustainable. We actively follow and iterate on these development principles to push us and our customers to build responsible and sustainable technology.

AI can make a significant contribution to manufacturing by faster action and decision making, innovative product, services and material designs, improved efficiency, higher precision and lower costs. It is time for a change. You can enable AI without the need for high capital investments or major process redesigns. You just need to look for the right opportunities to be more productive and sustainable.

In this blog, we will uncover some pressing challenges within the Life Science manufacturing industry and how breakthroughs in Machine Learning techniques offer measurable returns.