Moving Forward x ML6: Launch Your Career in AI

AI-powered Tumor Board uses specialized AI agents to analyze cancer cases, suggest treatments, and improve expert decision-making for complex pediatric leukemia and lymphoma cases.

ML6 attracts top AI talent with purpose-driven work, growth opportunities, and a transparent reward philosophy.

Discover how AI guardrails can ensure ethical and safe AI use. This blog explores key AI risks and provides practical strategies for implementing robust control mechanisms to protect your business.

Knowledge graphs can be a powerful way to connect complex data, but building them is notoriously time-consuming and difficult—especially in fields like biomedicine.

Why a GenAI Gateway is key for AI efficiency, governance, and innovation.

How ML6 uses feedback to drive innovation

Exploring Microsoft's Copilot for AI, RAG solutions, and custom automation capabilities.

Explore how AI agents bridge this gap by giving LLMs memory, tool access, and the autonomy to interact with systems like calendars and route planners.

Discover recent advancements in protein design with a focus on sequence generation.

Learn about AI Governance and risk management frameworks to ensure safe and ethical AI use in your organization.

Explore ESM-3's groundbreaking approach to sequence, structure, and function prediction in protein design.

ML6's journey in integrating Responsible AI, emphasizing ethics, legality, and security.

Guardrails for LLMs balance their capabilities and mitigate risks. Learn key techniques to ensure safety and accuracy.

How to navigate the requirements for developing high-risk AI systems within the regulation of the European AI Act, ensuring compliance and fostering ethical AI innovation.

Delve into how LLMs navigate real-time web information and the implications for their role in accessing dynamic content.

In this article we explain why the time for AI investments is now. We share our strategic framework to use AI in business transformation, aiming to maximise your AI investments.

Implementing ML models' code promotion across environments using CI/CD pipelines.

A company can only be as successful as the success of its people. Everything depends on a strong ‘people and talent approach’ that is committed to individual development and growth. At ML6, we consciously attract people who can bring new insights and skills. People who dare to think about innovation disruptively, but who always fit within our cultural values. So what does that look like more concretely you might wonder?

Discover how to optimize Retrieval Augmented Generation (RAG) by improving retriever performance, utilizing metrics for evaluation, and enabling follow-up questions for enhanced user experience.

Explore Google's latest AI model, Gemini, challenging GPT4.

Since the recent introduction of GPT-4 Vision, there has been a growing interest in multimodality. Read what does it mean for you.

A brief overview of what proteins are, their characteristics and the applications of protein engineering.

Optimizing OpenAI Whisper deployment in production.

How can model providers comply with regulation concerning training data & copyright?

Meta's LLAMA model takes center stage, but there's a catch: it's open-source, but not entirely. Here's the story of LLAMA, its recent updates, and what it means for you.

Discover the DGA's impact on data sharing, privacy, and innovation, and how ML6 solutions align with its objectives.

Unlock Machine Learning with Amazon SageMaker JumpStart: A Comprehensive Guide

In an era where digital interactions dominate, chatbots play a crucial role in enhancing user experience. Amazon Bedrock emerges as a beacon of simplicity, allowing developers to harness the power of cutting-edge machine learning models.

Fine-tune large language models for custom behavior and knowledge, weighing open source vs. closed source options.

Amazon recently announced their foundation model service: Amazon Bedrock. We got our hands on it as part of a limited preview. So, what’s in it for you?

At the end of college years, it can be very confusing to start determining your next steps. Are you 100% convinced about your choice of study? Do you have doubts about how your education plays out in practice? These questions certainly don't come as a surprise. There is a lot coming at you. For this reason, it is very interesting to explore the field in practice. Luckily, schools often offer that option through internships. 

Unlocking ASR Excellence: Fine-Tuning Whisper for Dutch Language

With the growing maturity of AI models and techniques, the technical art of the possible is no longer the most important ceiling.Company specific boundaries will come from the strategy of a company and the values it adheres to.

Reshaping Product Photography with generative AI

Understanding this behaviour and explaining why full control is unachievable

The magical world of ChatGPT, troves of information, and levels of automation.

Securing AI deployment in OT environments.

Addressing ethical considerations in LLM development.

Visualising Dynamic Pricing modelling to build insights

Discover the potential of Google's PaLM 2 as we harness generative language models for achieving business success.

Learn how dynamic pricing has revolutionized e-commerce through probabilistic statistics, iterative modeling, and customer feedback.

Learn how to make your home battery smarter by connecting it to external inputs such as energy prices and weather. This can save up to 35% on electricity costs.

Harness large foundation models affordably with LoRA technique for business applications.

ML6 tackles enzyme stability prediction in Kaggle competition, pushing boundaries in life sciences.

Unleash Astonishing Animations with Stability AI's Stable Animation SDK

Exploring Recent Developments in Open Source LLMs and Their Implications.

Different methods available for fine-tuning Large Language Models (LLMs) and when to use them.

AI image generation without copyright infringement using a Creative Commons dataset.

Comparing GCP and Azure from a developer's perspective.

Is AI only applicable within factories and large corporate organizations? Anything but. AI will (and already is) creating impact within the Human Resources branch as well. We speak with Julie Plusquin, Talent Partner at ML6 about what the evolution within HR may look like.

The structure of FMOps to optimize your foundation model workflows.

Introduction to Foundation Models and choosing the right one.

Google just released its Generative AI offering. What is it and what does it mean for you?

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

Train neural networks faster with JAX on TPUs

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

Homomorphic Encryption for Deep Learning with Intel’s HE-Transformer - Exploring the possibilities of hosting an ML model to perform inference on encrypted data using homomorphic encryption.

5 AI Applications to Enhance Retail Strategy Today

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

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

Realtime Tennis Action Recognition with Movinet Stream: Sports Video Analysis in the Real World

Building Future-Proof Data Architecture: Key Insights and Recommendations

Improving tennis court line detection using machine learning for near real-time predictions.

Assessing Detic for object detection of thousands of classes.

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

Google's EdgeTPU Dev Board surpasses Intel's Movidius Stick with a >10x speedup in inference time.

Learn how to use the ByT5 model for OCR correction, a token-free model that operates on raw bytes of text.

Open Sourcing Dutch Summarization Datasets for Transfer Learning

Implementing Zero Trust Architecture for Secure Remote Work

Jens Bontinck shares his view on a recent technique called Reflexion

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.

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.

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.

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.

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

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.

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

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

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

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, 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!

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.

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.

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.

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

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

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.

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

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.

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

A practical guide to generating artificial data using Unity Perception.

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

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

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

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.

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?

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.

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