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

The benefits of using managed Kubernetes, such as Google Kubernetes Engine (GKE), with contrast to self-managed Kubernetes are beyond dispute. However, it is not always feasible to leverage managed Kubernetes solutions. You might be restricted to on-premise machines because of legal constraints or have specific kubernetes add-ons (e.g. GPU sharing) that cannot be installed on top of managed Kubernetes.

Ipsum rem eaque rerum ad ipsam asperiores sit numquam ipsa. Sit consequuntur tempore perferendis dignissimos distinctio nulla ex. Voluptates non sit quo omnis. Quod libero laboriosam. Non rerum architecto laboriosam aut voluptate blanditiis ut odio quam. Est repudiandae quo ut magnam ullam soluta d

Part 1 - 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! 

Temporibus voluptate nihil nihil corporis sequi dicta cum dolor. Corporis rerum eaque veritatis voluptatem. Eum mol

As ML6 is a digital-native and agile company by design we can shift to a WFH policy without much hassle. We need to not lose speed and efficiency during this crisis. This puts us in the ideal spot to help companies learn how to get this digital-native mindset and learn some in-the-field best practices for digital transformation. We help them ensure business and innovation continuity during these unusual times. And organizing a digital workshop is one of these problems we had to overcome.

Model performance has increased dramatically over the last few years due to an abundance of machine learning research. While these improved models open up new possibilities, they only start providing real value once they can be deployed in production applications. This is one of the main challenges the machine learning community is facing today.

Quidem pariatur qui ut qui iste aperiam quidem sunt enim. Consequatur et aut. Totam officiis illum. Fugiat commodi corporis in. Doloribus in et ea a. Cupiditate inventore quia saepe sit officiis amet minus ea. Temporibus qui quia qui. Sed tempore har