Discovering Generative AI and Foundation Models with ML6 & AWS

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.

In this webinar, we explored the benefits and challenges of the fine-tuning approach, discovering when to opt for fine-tuning, outlining the different techniques and offering practical guidelines for getting started with fine tuning.

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.

AI-Enhanced Product Photography: Revolutionizing E-Commerce Visuals

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.

Learn how intelligent technology can optimize procurement processes with insights from Iron Mountain, Google Cloud, and ML6.

ML6 shares insights and learnings on how to leverage AI for Industry 4.0 based on our experiences with world-leading manufacturers.

Overcome manufacturing challenges with AI and Google Cloud. Learn from practical ML6 use cases and leverage machine learning retraining programs for improved operations.

Discover the importance of Explainable AI (XAI) in understanding machine learning models and explore practical case studies, including Google's Vertex Explainable AI tool.

Learn how to improve training data quality and maximize AI model accuracy in our webinar on data-centric AI, with practical tips and advanced approaches.

Discover how AI is driving sustainability in energy systems and manufacturing, with pragmatic use cases for decarbonization and waste reduction.

Explore the field of Hybrid AI, combining machine learning and expert rules for optimal outcomes. Learn its relevance, integration methods, and real-world examples.

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

Recording of the webinar on Generative AI for corporate use: How to get started with Large Language Models

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.

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

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

Applications of using VQGAN combined with Discrete Absorbing Diffusion models.

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.

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.

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.

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!

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.

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.

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

In this blogpost, we’ll explain how AI can help solve typical challenges in the commercial model of life sciences companies.

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