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.

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 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.

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.