Thoughts and Trends in Tech

Generative AI as your co-pilot: specialized AI models and their impact on design innovation.

Discover 3D optical acquisition methods: Stereo Vision, Structured Light, Time of Flight, and LiDAR.

Discover the power of 3D computer vision and its impact on industries. Learn about advantages, applications, and trends.

In this blogpost we explore the different options out there around usage of ChatGPT and other GPT models. For every option we we determine the risks around compliance, security and privacy.

In this article, we delve into the mind of Kaat Van Doren, Head of Talent & Culture at ML6, as she explores the role of curiosity within the organization and her own professional life at a podcast sit down with Cevora.

LoRA is revolutionizing fine-tuning for large AI models, enabling efficient adaptation without straining resources. Explore its principles, effectiveness, and impact on the open-source community.

Discover key amendments proposed in the EU AI Act, including banned and high-risk applications, regulations for foundation models and generative AI, and the impact on open-source components. Prepare for upcoming AI regulations and their implications.

Explore the potential of large language models (LLMs) for businesses: models, tooling, challenges, and applications

This blog post explores the process of labelling speech data for Automatic Speech Recognition (ASR).

We'll cover the basics of language models and how they can be trained on your organization's data to improve search accuracy, automate tagging, and even generate new content

Jens Bontinck, Head of Delivery & Advice, shares his view on the status quo of AutoGPT. In this blogpost, you will not learn how to run it, nor will I go into details of the technical solution. Therefore, if you are a product owner, a functional analist, or a commercial enthousiast, this should be right up your alley. Happy to read your feedback!

Discover the truth behind the hype of ChatGPT & GPT-4. Read about the potential game-changing capabilities of GPT-4 or if it's just another gimmick.

Jens Bontinck shares his view on a recent technique called Reflexion

How we empower our agents in their own personal road to success.

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

A guide on how to choose the right speaker diarization framework for your use case.

Tips on figuring out how to navigate the recruitment process and determine which companies are a good match for you.

This blog post explores the current state of AutoML tools and their ability to deliver on the promise of automating machine learning tasks without expert knowledge.

Apache Beam has lots of I/O connectors available, but not all connectors are in your language of choice.

Key take-aways of Google Cloud’s new Visual Inspection AI tool.

Explore the practical implementation of dynamic pricing, its effectiveness, challenges, and the use of Thompson sampling to optimize prices.

Examples of AI use cases for climate action and clean energy and sustainable industry and agriculture.

Examples of AI use cases for transport & mobility and environment & oceans.

Identified gaps in the architectures for SLR/SLT by considering and exploiting existing state-of-the-art architectures.

Semantic search from a business point of view and how it can bring value to your business processes.

This blogpost covers the need for MLOps and how it helps to manage the complexity of machine learning models and deployments. Secondly, it addresses the business implications of implementing MLOps as a proper ecosystem. Lastly, the challenges of building an MLOps capability are covered to ensure the success of your machine learning initiatives at your organisation.

Deploying a Scikit-learn Pipeline + LightGBM Classifier Model on Vertex AI for Explainable Online Predictions

Common hybrid NLP design patterns and look at example situations of when you should opt for which pattern.

How to approach uncertainty in machine learning : forecasting, regression and classification models.

When deploying a Transformer model, the complexity increases since besides the model one also needs to consider how to deploy the tokenizer. Is it best to deploy the tokenizer on the server or to have the end users handle it and what is the best way to deploy the tokenizer along with the model on the server so that inference latency and costs are minimized?

What do we do though if we don’t have any or enough usable data yet to get started? One obvious option is to collect (more) data. However, today we want to go beyond that. We will look at three other dimensions that can be relevant to unblock ML use cases: data protection, external data, and synthetic data.

Applications of using VQGAN combined with Discrete Absorbing Diffusion models.

We review and compare the previous SOTA generative models, from autoencoders to autoregressive models, and work our way to the discrete absorbing diffusion model.

Developing a DALL-E-based logo generator.

Detecting errors in transformers generated abstractive summaries

How we tackle information management at ML6 and how our tech radar helps us do this.

In this blogpost we will discover the complexity of pooling that hides behind its apparent simplicity.

Learn more about the similarities and differences between transformers and sentence-transformers.

A practical guide to generating artificial data using Unity Perception.

Insights in how to run your Transformer in the cloud in a cost-effective way.

How to maximise the value of data to drive better performance of ML models while minimising the cost of doing so.

Assessing Detic for object detection of thousands of classes.

Together with SWISS KRONO, ML6 identified the different requirements for AI use cases. Few key requirements are a solid data architecture and infrastructure, multi-disciplinary teams and management/organisation support.

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!

Abbreviated version of the AI act proposal : what you need to know.

Open Sourcing Dutch Summarization Datasets for Transfer Learning

A practical overview on information retrieval, in particular Semantic Search.

Why we created an Ethical AI unit at ML6 and what we learned so far.

In this blogpost we take a look at what data augmentation is and how you can efficiently discover good data augmentation strategies for your model and your use case.

A short introduction to unsupervised anomaly detection and how to apply it.

In light of a partnership with the organizations Reef Support and Fruitpunch AI, we designed an image segmentation algorithm. The main challenge here was the way the data is labelled, containing sparse pixel annotations created by domain experts. The blogpost covers on a high level how we solved this challenge, and how the challenge will be going forward.

In this blogpost, we look at object detection using 3D ‘depth’ sensor data. We explain how two recent approaches, VoteNet and 3DETR, tackle the problem and benchmark their performance out of the box and after fine-tuning.

Train neural networks faster with JAX on TPUs

Introduction blogpost to JAX : how it works, how it is different from Tensorflow/Pytorch and why we think every machine learning engineer should add it to its arsenal of tools.

We explore how the connexion library facilitates API-first design with Python andtake a look at what the advantages are of API-first and compare with the opposite approach of “code-first”. Based on a benchmarking exercise and our values, we will dive deeper into why we are helping to maintain connexion.

We turn the wheel and make the most unproductive week, the most productive week of the year with our XMas Projects.

Deepstream 6.0 Python Boilerplate for Custom AI-based Multi-Sensor Processing and Video Understanding

Learn about three ways that knowledge graphs and machine learning reinforce each other.

Pretrained TabTransformer performs well in unsupervised pretraining on tabular data, but LightGBM outperforms it on small labeled datasets.

Businesses in industries such as media, finance, jurisdiction and many more, tend to accumulate a large quantity of medium to long documents. Processing these documents can be challenging. We'll deep dive into the three main trends here.

A practical guide to efficient neural networks: model compression techniques including pruning, quantization, knowledge distillation, and optimization tricks like gradient checkpointing and accumulation.

Learn how to structure your data with knowledge graphs in this introduction blogpost to knowledge graphs and its business applications.

We are often asked how to detect very small objects in high resolution, i.e. very large images. A good example is finding objects in aerial images. The goal of this blog post is to demonstrate a practical approach to this problem using Slicing Aided Hyper Inference (SAHI).

In this blogpost, our ML engineer Juta will dive into the world of multimodal AI. Multimodal AI is able to reason about different modalities, such as text and images, at the same time and leads to more general understanding across domains.

Data Engineer Liam takes a look at how Pipelines can be deployed on the new and improved GCP product, Vertex AI Pipelines. The recently released tool takes a more managed approach to Pipelines in GCP, which promises to make running MLOps pipelines in the cloud easier than ever.

Our Head of Ethical AI , Caroline , takes a look at why explainability is important when developing AI solutions, and how to think about implementing Explainable AI within the entire ML project cycle.

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.

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.

Beyond Frames: Unlocking the Power of Video Analysis

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 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.

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 blogpost, we tackle 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.

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’ll explain how AI can help solve typical challenges in the commercial model of life sciences companies.

What is Ethical and Trustworthy AI is and how do we at ML6 help our clients unlock the full value of AI and build trust with their customers, employees and society as a whole.

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.

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.

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.

Improving Python Interfaces with Keyword-Only Arguments

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.

This blog post explains and demonstrates how to optimize the process of object detection when aiming for high accuracy using Google’s lightweight EfficientDet object detection model.

5 AI Applications to Enhance Retail Strategy Today

Exploring Google's Edge TPU: What it is, how it works, and why it matters

Log4Shell got security teams around the world on their toes In this blog post, we take a look at how and why we built a live threatmap on top of Google Cloud to detect and visualize cyber attacks and log4j exploits.

We will guide you through the technical architecture of TalkNet to provide you with a basic technical understanding. Then, we will show you how you can train this yourself based on your own data.