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.

A listed sports media company, broadcasting numerous sporting events live via an online platform.

Analytic Engineers? Analytic Engineering? ‍Another new term in the data world or is it an example of clever marketing? Analytic Engineering is a term pushed by the team behind Dbt, Data Build Tool. Dbt, and the recently acquired DataForm, are tools that simplify data engineering and data governance for data pipelines expressed in SQL.

In June 2020 McKinsey published a report called “How to build a data architecture to drive innovation — today and tomorrow”. The report is an excellent view of modern future proof data architecture. The article highlights the advances in data technology and the recommended building blocks and methodologies.

The Neural Information Processing Systems (NeurIPS) conference is a meeting that revolves around machine learning and computational neuroscience. Jules Talloen and Thomas Uyttenhove, both Machine Learning Engineers at ML6, have gotten a paper submission accepted at NeurIPS workshops. For this reason, they attended the virtual NeurIPS 2020 conference and documented trends and relevant topics that we encountered. This blogpost will go over ML6’s workshop contributions and will give you a closer look at some of the talks and demonstrations that caught their eye.

A short while ago, we were happy to receive the news that ML6 won the 2020 ‘AI Innovator of the Year’ award in the prestigious Data News Awards for Excellence. To celebrate our award, we would like to share some behind the scene on how we foster innovation at ML6.

Exploring the effectiveness of recently released face anonymization algorithms

A tale of multilingualism, summarization and sentence embeddings