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.

In this blogpost, we tackel the challenge of picking the right camera for your computer vision projects by outlining a few key camera characteristics and features that will help you wit your camera decision.

The customer story of ASML and ML6 using Google Cloud to on-premises machine learning solutions.

In this blogpost, we’ll explain how AI can help solve typical challenges in the commercial model of life sciences companies.

In this blog post we explain what Ethical and Trustworthy AI is and how we at ML6 help our clients unlock the full value of AI and build trust with their customers, employees and society as a whole.

Part 2- The rise of artificial intelligence has become omnipresent in recent years, state-of-the-art models are open-sourced on a daily basis and companies are fighting for the best data scientists and machine learning engineers, all with one goal in mind: creating tremendous value by leveraging the power of AI. Sounds great, but reality is harsh. Models, in general, don’t make it to production, and even if they do, inconsistencies along the way prevent your model from doing that one thing it was supposed to do: generate value. Let’s solve that. Once and for all. Unleash the power of Tensorflow Extended by building a production-ready sentiment analysis model! 

In this blogpost we cover the most important aspects of tackling a computer vision problem by labeling your own data.

MLOps is a trending topic within the field of AI. Yet, for someone new to the topic it’s quite a difficult term to grasp. This post explains MLOps in simple terms and outlines why you should consider deploying it in your organization.

As we often get questions about our MLOps best practices and the tech stack we use, we gladly give a short overview of how we apply MLOps at ML6 below.

With our MLOps Advisory services, we collaborate with our clients, their business and IT teams to integrate MLOps best practices into their organization to increase the success rate of their machine learning projects and successfully unlock the value of AI.

Let’s look into a few pitfalls that we’ve encountered in our MLOps advisory services and ML in production projects if you scale AI without the right team, budget and MLOps. 

After transformers disrupted natural language processing and computer vision, they now set their eyes on the biggest data type out there: tabular data. In this blog post, we will have a 10 000 feet (3048 m) view at TabTransformer, a paper by Amazon Web Services. First, we'll answer why we can apply transformers to tabular data. Then, we'll see how they rock tabular data.

ML6 worked together with CNH to accelerate the integration of their AI algorithms into existing embedded hardware. The goal was to incorporate functionality so that the combine can automatically alter its internal parameters to optimise harvest quality. The combines are equipped with a camera and hardware to detect the quality of the crop that is being harvested by running image segmentation on the hardware and images taken by the camera.

With our Advisory services, we support multiple clients in speeding up the adoption of AI and Advanced Analytics by giving concrete recommendations for their data & cloud architecture and application landscape. In Koen's latest blogpost, he explains our view on the modern enterprise team and shares five tips and tricks on how to make the most of your data and organize for success.

Data is the “fuel” for ML. Yet, in many projects, it is still challenging to get the data right due to a lack of documentation, data quality issues, missing historical data, scalability issues with data platforms and overall lack of ownership.

In this blogpost, we will use a new, user friendly technique for time series data mining: calculating the matrix profile. Unfortunately, we can’t reveal the real data or application, so we’ll substitute the original problem with a new one. To conclude we will lay out the pros and cons of gthe matrix profile.

Saroléa Motorcycles Saroléa is an automotive engineering company, specialised in the development of high-performance electric vehicles.

In pursuit of continuous customer satisfaction, the company's devices are cloud-connected to enable data to be gathered on product usage, component status, settings and presets, and environmental parameters. The company had an analytics pipeline in place to process this data but wanted to explore further possibilities with Google Cloud, machine learning and open source software in general.

The goal of this project was to predict if a patient needed a pacemaker, based on an echocardiogram of the heart.