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Sharon Grundmann \- Developing a Large-scale Creative Commons Dataset for
Image
In January 2023, Getty Images sued Stability AI for training Stable Diffusion
on over 12 million of their copyrighted images. This is a recurrent problem
with foundation models which are trained on vast image datasets from the
Internet, which often include copyrighted content.
In this talk, we will present our solution to this problem – building a large-
scale dataset of Creative Commons licensed images. We will dive into the
challenges and how we leverage Fondant, an open-source framework that
democratises data preparation for foundation models, to realise this.
More information & registration here.
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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.
February 13, 2024
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February 13, 2024
By
Jens Bontinck
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Implementing ML models' code promotion across environments using CI/CD
pipelines.
February 8, 2024
By
Miro Goettler
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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?
February 5, 2024
By
Julie Plusquin
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Explore the impact of the EU AI Act on businesses, focusing on the interplay
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Explore Google's latest AI model, Gemini, challenging GPT4.
December 7, 2023
By
Matthias Feys | scraping/output/-5857196853429635125.txt | [
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Optimizing OpenAI Whisper deployment in production.
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How can model providers comply with regulation concerning training data &
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October 10, 2023
By
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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.
October 5, 2023
By
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Discover the DGA's impact on data sharing, privacy, and innovation, and how
ML6 solutions align with its objectives.
October 2, 2023
By
Michiel Van Lerbeirghe
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Unlock Machine Learning with Amazon SageMaker JumpStart: A Comprehensive Guide
September 28, 2023
By
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Unlock Machine Learning with Amazon SageMaker JumpStart: A Comprehensive Guide
September 28, 2023
By
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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.
September 20, 2023
By
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Fine-tune large language models for custom behavior and knowledge, weighing
open source vs. closed source options.
February 20, 2024
By
Michiel De Koninck
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February 20, 2024
By
Michiel De Koninck
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This is some text inside of a div block.
This is some text inside of a div block.
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?
September 6, 2023
By
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This is some text inside of a div block.
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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.
September 6, 2023
By
Julie Plusquin
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September 6, 2023
By
Julie Plusquin
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This is some text inside of a div block.
This is some text inside of a div block.
Unlocking ASR Excellence: Fine-Tuning Whisper for Dutch Language
September 5, 2023
By
Sharon Grundmann
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This is some text inside of a div block.
This is some text inside of a div block.
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.
August 22, 2023
By
This is some text inside of a div block.
This is some text inside of a div block.
This is some text inside of a div block.
Reshaping Product Photography with generative AI
August 23, 2023
By
Bert Christiaens
This is some text inside of a div block.
This is some text inside of a div block.
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August 23, 2023
By
Bert Christiaens
This is some text inside of a div block.
This is some text inside of a div block.
This is some text inside of a div block.
Understanding this behaviour and explaining why full control is unachievable
August 22, 2023
By
Michiel De Koninck
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This is some text inside of a div block.
This is some text inside of a div block.
The magical world of ChatGPT, troves of information, and levels of automation.
August 10, 2023
By
Jens Bontinck
This is some text inside of a div block.
This is some text inside of a div block.
This is some text inside of a div block.
Securing AI deployment in OT environments.
August 3, 2023
By
Rob Vandenberghe
This is some text inside of a div block.
This is some text inside of a div block.
This is some text inside of a div block.
Addressing ethical considerations in LLM development.
August 8, 2023
By
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July 24, 2023
By
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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.
July 16, 2023
By
Juta Staes
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Harness large foundation models affordably with LoRA technique for business
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July 13, 2023
By
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ML6 tackles enzyme stability prediction in Kaggle competition, pushing
boundaries in life sciences.
July 10, 2023
By
Pieter Coussement
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July 10, 2023
By
Pieter Coussement
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Unleash Astonishing Animations with Stability AI's Stable Animation SDK
June 6, 2023
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Exploring Recent Developments in Open Source LLMs and Their Implications.
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Different methods available for fine-tuning Large Language Models (LLMs) and
when to use them.
June 19, 2023
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AI image generation without copyright infringement using a Creative Commons
dataset.
June 29, 2023
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This is some text inside of a div block.
This is some text inside of a div block.
AI image generation without copyright infringement using a Creative Commons
dataset.
June 29, 2023
By
Philippe Moussali
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This is some text inside of a div block.
This is some text inside of a div block.
Comparing GCP and Azure from a developer's perspective.
June 26, 2023
By
Michiel De Koninck
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This is some text inside of a div block.
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.
July 20, 2023
By
Julie Plusquin
This is some text inside of a div block.
This is some text inside of a div block.
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July 20, 2023
By
Julie Plusquin
This is some text inside of a div block.
This is some text inside of a div block.
This is some text inside of a div block.
The structure of FMOps to optimize your foundation model workflows.
June 7, 2023
By
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This is some text inside of a div block.
Introduction to Foundation Models and choosing the right one.
June 7, 2023
By
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Learn how intelligent technology can optimize procurement processes with
insights from Iron Mountain, Google Cloud, and ML6.
April 21, 2021
By
Robbe Sneyders
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April 21, 2021
By
Robbe Sneyders
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ML6 shares insights and learnings on how to leverage AI for Industry 4.0 based
on our experiences with world-leading manufacturers.
May 27, 2021
By
Jeffrey Hagen
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Overcome manufacturing challenges with AI and Google Cloud. Learn from
practical ML6 use cases and leverage machine learning retraining programs for
improved operations.
September 2, 2021
By
Arne Vandendorpe
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Discover the importance of Explainable AI (XAI) in understanding machine
learning models and explore practical case studies, including Google's Vertex
Explainable AI tool.
March 9, 2022
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Discover the importance of Explainable AI (XAI) in understanding machine
learning models and explore practical case studies, including Google's Vertex
Explainable AI tool.
March 9, 2022
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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.
June 9, 2022
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Discover how AI is driving sustainability in energy systems and manufacturing,
with pragmatic use cases for decarbonization and waste reduction.
November 24, 2022
By
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November 24, 2022
By
Jeffrey Hagen
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Explore the field of Hybrid AI, combining machine learning and expert rules
for optimal outcomes. Learn its relevance, integration methods, and real-world
examples.
October 17, 2022
By
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Google just released its Generative AI offering. What is it and what does it
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June 5, 2023
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Recording of the webinar on Generative AI for corporate use: How to get
started with Large Language Models
June 2, 2023
By
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June 2, 2023
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December 12, 2022
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December 12, 2022
By
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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.
July 16, 2019
By
Julien Schuermans
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This is some text inside of a div block.
This is some text inside of a div block.
5 AI Applications to Enhance Retail Strategy Today
July 10, 2019
By
Stan Callewaert
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This is some text inside of a div block.
This is some text inside of a div block.
Exploring Google's Edge TPU: What it is, how it works, and why it matters
March 14, 2019
By
Stan Callewaert
This is some text inside of a div block.
This is some text inside of a div block.
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March 14, 2019
By
Stan Callewaert
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This is some text inside of a div block.
This is some text inside of a div block.
Deepstream 6.0 Python Boilerplate for Custom AI-based Multi-Sensor Processing
and Video Understanding
January 17, 2022
By
Jules Talloen
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This is some text inside of a div block.
Realtime Tennis Action Recognition with Movinet Stream: Sports Video Analysis
in the Real World
October 15, 2021
By
Jules Talloen
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This is some text inside of a div block.
This is some text inside of a div block.
Building Future-Proof Data Architecture: Key Insights and Recommendations
February 11, 2021
By
Robbe Sneyders
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This is some text inside of a div block.
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February 11, 2021
By
Robbe Sneyders
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Improving tennis court line detection using machine learning for near real-
time predictions.
March 13, 2023
By
Jules Talloen
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Assessing Detic for object detection of thousands of classes.
May 17, 2022
By
Matthias Cami
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A practical guide to efficient neural networks: model compression techniques
including pruning, quantization, knowledge distillation, and optimization
tricks like gradient checkpointing and accumulation.
November 24, 2021
By
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By
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Google's EdgeTPU Dev Board surpasses Intel's Movidius Stick with a >10x
speedup in inference time.
March 13, 2019
By
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Learn how to use the ByT5 model for OCR correction, a token-free model that
operates on raw bytes of text.
December 15, 2021
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Open Sourcing Dutch Summarization Datasets for Transfer Learning
May 12, 2022
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Implementing Zero Trust Architecture for Secure Remote Work
March 24, 2020
By
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This is some text inside of a div block.
Implementing Zero Trust Architecture for Secure Remote Work
March 24, 2020
By
Rob Vandenberghe
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This is some text inside of a div block.
This is some text inside of a div block.
Jens Bontinck shares his view on a recent technique called Reflexion
April 13, 2023
By
Jens Bontinck
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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.
May 17, 2023
By
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May 17, 2023
By
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By
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May 17, 2023
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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.
May 17, 2023
By | scraping/output/-5857196853429635125.txt | [
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May 17, 2023
By
Rob Vandenberghe
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This is some text inside of a div block.
This is some text inside of a div block.
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
May 9, 2023
By
Michiel De Koninck
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This is some text inside of a div block.
This is some text inside of a div block.
This blog post explores the process of labelling speech data for Automatic
Speech Recognition (ASR).
May 10, 2023
By
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This is some text inside of a div block.
This is some text inside of a div block.
How we empower our agents in their own personal road to success.
March 31, 2023
By
Julie Plusquin
This is some text inside of a div block.
This is some text inside of a div block.
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March 31, 2023
By
Julie Plusquin
This is some text inside of a div block.
This is some text inside of a div block.
This is some text inside of a div block.
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.
April 14, 2023
By
This is some text inside of a div block.
This is some text inside of a div block.
This is some text inside of a div block.
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!
April 18, 2023
By
Jens Bontinck
This is some text inside of a div block.
This is some text inside of a div block.
This is some text inside of a div block. | scraping/output/-5857196853429635125.txt | [
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April 18, 2023
By
Jens Bontinck
This is some text inside of a div block.
This is some text inside of a div block.
This is some text inside of a div block.
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.
November 1, 2022
By
This is some text inside of a div block.
This is some text inside of a div block.
This is some text inside of a div block.
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. | scraping/output/-5857196853429635125.txt | [
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January 20, 2022
By
Ruwan Lambrichts
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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.
April 14, 2021
By
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How we tackle information management at ML6 and how our tech radar helps us do
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July 6, 2022
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Georges Lorré
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A guide on how to choose the right speaker diarization framework for your use
case.
March 10, 2023
By
Philippe Moussali
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March 10, 2023
By
Philippe Moussali
This is some text inside of a div block.
This is some text inside of a div block.
This is some text inside of a div block.
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.
January 3, 2023
By
Julien Schuermans
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This is some text inside of a div block.
This is some text inside of a div block.
We turn the wheel and make the most unproductive week, the most productive
week of the year with our XMas Projects.
January 19, 2022
By
This is some text inside of a div block.
This is some text inside of a div block.
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January 19, 2022
By
This is some text inside of a div block.
This is some text inside of a div block.
This is some text inside of a div block.
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.
August 31, 2021
By
Liam Campbell
This is some text inside of a div block.
This is some text inside of a div block.
This is some text inside of a div block.
Apache Beam has lots of I/O connectors available, but not all connectors are
in your language of choice.
December 15, 2022
By
Andres Vervaecke
This is some text inside of a div block.
This is some text inside of a div block.
This is some text inside of a div block.
A practical guide to generating artificial data using Unity Perception.
May 22, 2022
By
Jules Talloen | scraping/output/-5857196853429635125.txt | [
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This is some text inside of a div block.
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A practical guide to generating artificial data using Unity Perception.
May 22, 2022
By
Jules Talloen
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Deploying a Scikit-learn Pipeline + LightGBM Classifier Model on Vertex AI for
Explainable Online Predictions
October 26, 2022
By
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We review and compare the previous SOTA generative models, from autoencoders
to autoregressive models, and work our way to the discrete absorbing diffusion
model.
August 11, 2022
By
Bert Christiaens
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August 11, 2022
By
Bert Christiaens
This is some text inside of a div block.
This is some text inside of a div block.
This is some text inside of a div block.
How to maximise the value of data to drive better performance of ML models
while minimising the cost of doing so.
May 17, 2022
By
This is some text inside of a div block.
This is some text inside of a div block.
This is some text inside of a div block.
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.
September 6, 2022
By
Matthias Feys
This is some text inside of a div block.
This is some text inside of a div block.
This is some text inside of a div block. | scraping/output/-5857196853429635125.txt | [
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September 6, 2022
By
Matthias Feys
This is some text inside of a div block.
This is some text inside of a div block.
This is some text inside of a div block.
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?
September 6, 2022
By
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This is some text inside of a div block.
This is some text inside of a div block.
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.
February 3, 2022
By
Jasper Van den Bossche
This is some text inside of a div block.
This is some text inside of a div block. | scraping/output/-5857196853429635125.txt | [
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February 3, 2022
By
Jasper Van den Bossche
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In this blogpost we will discover the complexity of pooling that hides behind
its apparent simplicity.
June 22, 2022
By
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Semantic search from a business point of view and how it can bring value to
your business processes.
November 7, 2022
By
Sven Rymenans
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A practical overview on information retrieval, in particular Semantic Search.
May 9, 2022
By
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Applications of using VQGAN combined with Discrete Absorbing Diffusion models. | scraping/output/-5857196853429635125.txt | [
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This is some text inside of a div block.
This is some text inside of a div block.
Applications of using VQGAN combined with Discrete Absorbing Diffusion models.
August 30, 2022
By
Bert Christiaens
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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.
November 3, 2020
By
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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.
April 28, 2021
By
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April 28, 2021
By
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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.
November 24, 2021
By
Thomas Vrancken
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October 31, 2023
# Assessing the impact of the EU AI Act proposal
Contributors
Michiel Van Lerbeirghe
Legal counsel and responsible AI consultant
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### How can model providers comply with regulation concerning training data &
copyright?
Foundation models play a pivotal role as the foundation of many advanced
artificial intelligence systems. Simply put, a foundation model is a large-
scale AI model trained using vast amounts of data, that serves as a basis for
further specialisation or application in varied domains. One prominent example
of such systems are generative AI models, which have the capability to
autonomously produce content, be it text (like ChatGPT), images (like
Midjourney), audio, or video.
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Generative AI models, due to their very nature, rely on extensive datasets for
training. These datasets often contain giant amounts of images, text snippets,
and other forms of data gathered from diverse sources. The sheer volume and
variety of data these models consume can sometimes overshadow the origin of
the data, some of which might be protected under copyright laws.
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The EU AI Act aims to regulate foundation models and generative AI systems.
Pursuant to one of the obligations imposed by the current version of the Act
by the European Parliament (the text is not finalised yet), providers of
foundation models used in generative AI systems should "document and make
publicly available a sufficiently detailed summary of the use of training data
protected under copyright law" (amendment 399, article 28b of the current
text). In other words, companies such as OpenAI (as the provider of ChatGPT)
would be obliged to document and disclose the copyright-protected data they
used to train their models.
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The goal of the obligation is clear and logical, namely providing transparency
and ensuring that stakeholders have visibility into the workings of these
influential AI systems. However, and whilst we support more transparency, we
will raise two reasons in this blogpost why the envisaged obligation could
prove to be a very difficult (not to say impossible) task for many companies
working on developing and deploying these kinds of models:
1. Copyright can go very far and a lot of different content could potentially be protected by copyright (books, images, text snippets, design objects, the design of functional objects,...). The obligation would therefore create huge administrative burdens on providers of foundation models as an incredible huge amount of content should be documented and disclosed. | scraping/output/6652929560987878043.txt | [
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2. The question whether or not the conditions for copyright protection are met is subjective, making the applicability of copyright protection unpredictable. Moreover, the providers of foundation models are not in the best position to rule whether or not the conditions are met.
As the consequences of non-compliance could lead to severe penalties, we
believe that, if the provision would actually be accepted, more guidance is
needed on how providers of foundation models can comply with this obligation.
### Copyright can reach far
Copyright protection applies to "works of art and literature", which is an
autonomous concept of Union law to be interpreted uniformly throughout the
European Union.
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Copyright protection applies to "works of art and literature", which is an
autonomous concept of Union law to be interpreted uniformly throughout the
European Union.
For the interpretation of the term, we need to look at the case law of the
Court of Justice of the European Union ("CJEU"). According to the case law, we
can speak of a protected work when two conditions are met: (1) the work needs
to be original and (2) there needs to be an expression (for example CJEU 12
September 2019, Cofemel, C-683/17, para. 29):
1. First, the concept “work” entails that there exists an original subject matter, in the sense of being the author’s own intellectual creation.
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1. First, the concept “work” entails that there exists an original subject matter, in the sense of being the author’s own intellectual creation.
It follows from the Court’s settled case-law that, if a subject matter is to
be capable of being regarded as original, it is both necessary and sufficient
that the subject matter reflects the personality of its author, as an
expression of his free and creative choices (see for example CJEU 1 December
2011, Painer, C‑145/10, paragraphs 88, 89 and 94).
2. Secondly, classification as a work is reserved to the elements that are the expression of such intellectual creation. It must be possible to clearly and precisely identify the subject matter, without having an element of subjectivity. This condition is for example the reason why 'a taste' cannot be protected by copyright, as a taste will be subjective, and cannot be objectively identified (CJEU 13 November 2018, Levola Hengelo, C‑310/17, paragraphs 33 and 35 to 37).
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When subject matter complies with these two European conditions, it will be
protected by copyright. The conditions are sufficient, which implies that no
other conditions may be imposed for protection, making terms such as
'novelty', 'inventiveness', 'aesthetic or artistic character' or 'a certain
level of effort or expertise' irrelevant when determining whether subject
matter is protected by copyright or not.
Following these European conditions, a lot of subject matter could be
considered to be a work in the sense of copyright law. Examples from case law
prove that copyright protection can reach far and that the concept of a “work”
is broadly interpreted (next to the obvious subject matter such as books,
images, music works, videos, and so forth):
* For example, a Belgian court already ruled that a guide for using IT equipment was protected by copyright (Brussels Court of Appeal, 28 January 1997).
| scraping/output/6652929560987878043.txt | [
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* For example, a Belgian court already ruled that a guide for using IT equipment was protected by copyright (Brussels Court of Appeal, 28 January 1997).
* Or, according to the CJEU eleven consecutive words can potentially be a “work” and therefore protected by copyright (CJEU 16 July 2009, Infopaq, C-5/08).
* And, also the design of very functional objects could be considered to be a work of art and literature. For example in case law it was ruled that the design pictured below (the support of a waffle iron) was protected by copyright (Court of appeal Brussels 25 October 2011, no. 2011/AR/119):
The broad scope of protection has the consequence that the envisaged
obligation of the AI Act could be very extensive, leading to extreme
administrative burdens. For example, if there is a picture of a functional
object, it could be that (i) the picture as such is protected by copyright as
well as (ii) the design of the functional object.
| scraping/output/6652929560987878043.txt | [
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It goes without saying that a provider of Generative AI systems would have a
huge task in documenting and disclosing information regarding the copyright
protected subject matter, knowing that a Generative AI system can be trained
on millions of images, text snippets, drawings, books, ...
### Copyright is subjective
Further, it is important to note that copyright exists upon creation and
without registration, which means that there is also not a copyright register
that providers of generative AI systems could consult to check whether or not
certain data is copyright protected or not.
In practice, it will be up to a judge (usually in the context of litigation)
to rule whether or not a certain work meets the conditions. In doing so, it is
also up to the alleged right holder to prove that the subject matter is a work
of art and literature.
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We notice that there is a lot of subjectivity regarding the interpretation of
the conditions, making the applicability of copyright protection
unpredictable.
The following examples prove that the assessment can go either way:
* In a case regarding pictures of football players and football games (portrait pictures, pictures of players in action, pictures of the stadions and atmosphere) the court of appeal of Brussels ruled that these kinds of pictures were protected by copyright because the photographer was able to make several free and creative choices regarding the pictures, for example regarding the angle of the picture, the point of view, the lighting, the timing of the picture, the adjustment of the camera and so forth. The photographer therefore made "free and creative choices" while making the pictures, which means that these were original (Court of appeal Brussels, 3 October 2017, no. 2013/AR/860).
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* In another case however the court of appeal of Ghent ruled that the pictures used on a specific real estate website as well as the accompanying texts describing the properties were not protected by copyright. The court ruled that pictures could potentially be protected but that the originality was not proven in this specific case (Court of appeal Ghent, 25 June 2018, no. 2016/AR/470).
* And even regarding the same subject matter, a different court can have a different opinion, for example:
* Multiple courts already ruled that the design of the handbag Le Pliage from Longchamp was protected by copyright (inter alia Court of appeal Brussels on 18 May 2006, no. 2003/AR/880 and on 20 April 2012, no. 2012/AR/2910), while there is also case law ruling that the design is not protected (court of appeal Ghent, 20 October 2014, no. 2013/AR/1945).
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* Or regarding the following object, in first instance it was ruled that the design was protected by copyright (commercial court Ghent, 11 January 2018, no. A/16/02910) while the court of appeal ruled that it wasn’t (court of appeal Ghent, 1 February 2021, no. 2018/AR/254):
The examples above show that there is a lot of subjectivity regarding
copyright protection, and that even the opinion of judges may vary regarding
the subject matter.
Obviously, if even judges’ opinions may vary, it is very difficult for a
provider of generative AI systems to assess whether or not certain data is
copyright protected or not. All the more when there is no copyright register
where this can be checked.
### Conclusion
| scraping/output/6652929560987878043.txt | [
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### Conclusion
It goes without saying that we are in favour of more transparency on data for
foundation model providers. For example, we fully support the idea to regulate
foundation models and the envisaged transparency obligations to disclose
compute (model size, computer power, training time), the capabilities and
limitations of the model, the results of internal and external testing and so
forth. However, and while the provision on transparency regarding copyrighted
material is logical as well, we believe that the current provision is
difficult to comply with from a practical point of view for the reasons
mentioned in this blogpost.
If the provision would actually be implemented, we believe that more guidance
is needed on how providers can actually meet the envisaged obligation of the
AI Act.
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] |
If the provision would actually be implemented, we believe that more guidance
is needed on how providers can actually meet the envisaged obligation of the
AI Act.
Moreover, more guidance on how the concept of a “sufficiently detailed
summary” should be interpreted, would be welcome. The question arises how
sufficiently detailed the disclosure must be, and what is meant with a
summary.
The importance and the need of guidance is clear, as non-compliance with the
new provisions may expose generative AI system providers to liabilities if
insufficient summaries regarding training datasets are in place. A failure to
comply with these disclosure obligations could lead to potential fines of up
to €10 million or 2% annual turnover, whichever is higher.
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# Developing AI Systems in the Foundation Model Age : An intro
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### Introduction
Analysts believe that we are entering the industrial era of artificial
intelligence. Foundation models (FMs) — large pretrained AI models that can
easily be adapted to new use cases — are revolutionizing creative work and are
expected to augment or take over ever more knowledge work in the coming years
as ever more use cases in different industries are being tackled by FM-based
AI.
Companies in the creative and knowledge industries are scrambling to develop a
strategy as they sense that their business may well be in for a rollercoaster
ride in the coming years. Customers are reporting to us that, for some tasks,
their workers are already up to five times more productive using off-the-shelf
generative AI tools, performing a week’s work in a day. No one wants to stay
behind but for many it is not clear where or how to start. | scraping/output/1321182964810706237.txt | [
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In this blogpost, we try to make sense of some of the developments we see
occurring around us and propose a general strategy of how to think of and
approach the ongoing FM revolution. We will first have a look at FMs
themselves and how they are different from what came before. After that we
will look at MLOps and how it is giving way to Foundation Model Ops (FMOps)
which is much more about aligning model output rather than just performance
and stability. Overall, we believe that it is possible for companies to set up
data and AI infrastructure now in such a way that they will be able to jump on
any of the innovations that are bound to occur. The key will be to put solid
internal data management in place and start optimizing internal processes.
FMOps presents a key to set this evolution in motion.
### Gradually, then suddenly: Foundation Models
The term Foundation Model (FM) was coined in a 2021 report by researchers from
Stanford University and defined as follows:
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### Gradually, then suddenly: Foundation Models
The term Foundation Model (FM) was coined in a 2021 report by researchers from
Stanford University and defined as follows:
> A foundation model is a machine learning model trained on broad data at
> scale such that it can be adapted to a wide range of downstream tasks
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> A foundation model is a machine learning model trained on broad data at
> scale such that it can be adapted to a wide range of downstream tasks
While most agreed that this shift was indeed taking place, many in the
industry downplayed its significance as these new models were initially
confined to research labs and occasional demo applications. With the advent of
models like ChatGPT, however, it has become clear to everyone that we are
witnessing a fundamental paradigm shift. Previously, machine learning models
were trained to do a specific task and then chained with other models and
business logic to make decisions. FMs, however, are multi-billion-parameter
models pretrained on terabytes of often multimodal data (e.g. text and images)
using gigantic amounts of compute (e.g. LLaMA: 118 gpu-years) which are then
guided to perform complex tasks relatively independently. | scraping/output/1321182964810706237.txt | [
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] |
The Stanford researchers point to emergence and homogenization as useful
concepts for understanding the ongoing shift. As models grow larger and they
are trained on more data, they start to display emergent behavior. This means
that, while they have been trained to perform a very simple task such as
predict the next word or remove the noise from this image, they will develop
complex behaviors to do so. These more complex patterns like reading
comprehension (resembling human reasoning) or drawing like Van Gogh
(resembling human creativity) are never explicitly trained. They just emerge
from learning to reconstruct the data.
| scraping/output/1321182964810706237.txt | [
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Partially related to this is the fact that the shift towards foundation models
comes with a tendency towards homogenization: less diversity in the models
that are being used. As large models are expensive to train and can be adapted
downstream to perform a range of tasks, in the future, the industry will
likely rely on a limited number of foundation models driving a wide array of
applications. This comes with certain risks regarding societal bias and
misinformation. Hence, in the future one of the main challenges for machine
learning professionals will be to align model behavior not just in terms of
performance at certain tasks but also in terms of norms and values and human
expectations in general.
### Playing hard to get | scraping/output/1321182964810706237.txt | [
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### Playing hard to get
In the past few years, several dozen FMs have been developed, most of which
were generative AI models ‘translating’ from one modality to another, e.g.
text to text (GPT), text to image (DALL-E), image to text (BLIP), speech to
text (Whisper), text to 3D (DreamFusion), text to short video (Make A Video),
text to longer video (Phenaki), video to video (Gen1) and text to 3D video
(Make a video 3D). Connecting text and images (CLIP) and Segmentation (SAM)
are two examples of other tasks that were tackled by FMs.
These FMs are typically ‘released’ in one or several of three ways: | scraping/output/1321182964810706237.txt | [
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* Scientific paper: most FMs (by Meta, Google, Salesforce) are described in a scientific paper. Sometimes they are not made available in any other way which implies that they can only be used in applications when they are reimplemented based on the paper, e.g. by the open source community as in the case of Google Imagen / DeepFloyd IF.
* API access: paid or free API access through which you can interact with the FM: typically the models by OpenAI. Sometimes there is also a possibility to fine-tune the model on custom data, also through an API. Control is limited and prices can be steep however. | scraping/output/1321182964810706237.txt | [
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* Open Source: code for running and fine-tuning and weights are made available which can be used relatively freely, e.g. models by Meta, Salesforce, Stability AI, Hugging Face, research institutes, open source organizations (e.g. LAION, Eleuther). The main issue to take into account here is the license under which which can be restrictive (e.g. only for research) or permissive (also allowing commercial use). | scraping/output/1321182964810706237.txt | [
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It is unclear at this point which will be the paradigm under which most models
will be made available as a growing number of FMs enter the market.
Competition between different cloud and model providers will play an important
role in this respect, as will regulation probably, as exemplified by the
recent amendments to the EU AI act and the US Senate hearings. Based on
current offerings and announcements from various cloud providers, the most
likely scenario is that there will be a spectrum of configurations from very
limited control (simple prompting through an API) to fully open access to code
and weights for customization and fine-tuning.
### Choosing a Foundation Model
Previously, when building custom models, performance was determined by data
availability (quantity and quality), architecture and hyperparameter tuning.
Today, with FMs we see it largely come down to two largely independent
factors:
Base model performance: itself determined by | scraping/output/1321182964810706237.txt | [
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Base model performance: itself determined by
* Model size (number of parameters)
* Training duration
* Dataset size and quality
Fine-tuning performance: itself determined by
* Fine-tuning regimes (combination of): self-supervised, supervised, reward-based, …
* Dataset quality and size (multiple)
Choosing a base model directly affects your system performance and running
cost. Selecting a 33B parameter large language model for your setup will
likely improve performance, but it will also require more expensive
infrastructure. Interestingly, we see a tendency towards convergence of base
models in terms of model architecture, size and even training dataset.
Conceivably, in the future, we will end up with a range of base models which
are very similar and which will compete with each other in areas other than
performance such as price and licensing. | scraping/output/1321182964810706237.txt | [
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Hence we believe that, as we have seen with ChatGPT which went through
multiple stages of supervised and reward-based fine-tuning, that the
subsequent fine-tuning steps will become even more important determinants of
downstream task performance. Likely, fine-tuning itself will be further broken
down into upstream fine-tuning by model providers and downstream fine-tuning
on proprietary data and specific tasks by users. Hence we advise our customers
to primarily invest in downstream fine-tuning and general alignment power
while keeping other options as open as possible.
Apart from performance and running cost, the main factor in choosing a
foundation model is how easy and cost-effective it is to build a system around
it that satisfies your needs.
In our next blogpost, we go deeper into what it means to set up Foundation
Model Operations or FMOps.
###
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February 21, 2021
# [Trends] Analytic Engineering with Dbt or DataForm
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Share this post | scraping/output/-2606520620302271656.txt | [
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### Analytic Engineers? Or 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.
The main drivers behind this technology are the rise of cloud-based data
warehouses and the SQL knowledge available on the market.
Reference: https://mattturck.com/data2020/ — Data & AI Landscape 2020 by Matt
Turck
#### The shift towards cloud data warehouses | scraping/output/-2606520620302271656.txt | [
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] |
Reference: https://mattturck.com/data2020/ — Data & AI Landscape 2020 by Matt
Turck
#### The shift towards cloud data warehouses
In the data world, we’ve seen the age of challenging to scale relational
databases and data warehouse appliances, often on expensive on-premise
hardware.
Followed by the introduction of document or key-value databases with flexible
schemas but less powerful or more complex query capabilities.
Data processing shifted from traditional ETL tools to large scale distributed
data processing, for structured and unstructured data, on massive but
difficult to manage Hadoop/Spark clusters. These days cloud platforms and
independent vendors are simplifying cluster management in the cloud and
offering more flexibility to scale-out clusters based on demand. | scraping/output/-2606520620302271656.txt | [
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] |
On the other hand, modern cloud data warehouses, with an architecture split
into powerful but affordable column-based storage paired with flexible compute
are a game-changer.
It’s feasible to store large amounts of data and transform or query the data,
structured and semi-structured (JSON/XML), using SQL.
The cloud data warehouse automatically translates the SQL statement into
highly optimized distributed processing without having to write a single line
of cloud.
### The power of SQL
SQL, because of the declarative nature and knowledge available in the market,
is a continuous theme in the data and AI world.
Initially, data processing and machine learning were only possible by
developing programs in Java, Scala, Python etc. The trend these days is to add
SQL support to avoid or minimize programming. Great examples are Apache Kafka
with KSQL, Apache SparkSQL, FlinkSQL, BigQuery ML, BlazingSQL, BeamSQL. | scraping/output/-2606520620302271656.txt | [
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Why? SQL skills are easier to find in the market than developers with big data
or ML experience. People that work on data visualisations know, or learn SQL,
faster than Java or Python. Analysts which turn into data scientists often
prefer to use SQL instead of programming or relying on a separate data
engineering team.
### The problem with SQL
SQL can however be difficult to manage.
SQL statements end up in the database as views, scripts, stored procedures or
scheduled queries. Dynamic or reusable SQL is hard to write. Dependencies
between tables/views often need to be managed manually in cloud data
warehouses.
SQL statements mixed together with code can be messy.
Using a modern job orchestrator solves some of these problems because a
containerized step or operator can point to a versioned, parameterized, SQL
statement or script.
So we need to introduce a bit more software engineering best practices.
And that’s what Dbt and Dataform do. | scraping/output/-2606520620302271656.txt | [
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0.015099368058145046,
0.08656728267669678,
0.05371343716979027,
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] |
So we need to introduce a bit more software engineering best practices.
And that’s what Dbt and Dataform do.
### Dbt — engineering and data governance
Dbt combines configuration files, that describe the available source tables
and output artefacts (reports/notebooks/dashboards/…), with data
transformations, called models, written using templated SQL.
Everything is stored in files so it’s a no-brainer to version the files in Git
as any other code.
Using config files and variables, it’s easy to switch between different
environments.
Using materializations it’s a small code change to turn a view into a,
truncate/load or incremental/append-only, table.
By combining a bit of code, templates and SQL it’s not that complicated to
generate complex SQL based on configuration, variables or query results.
Dbtvault, to store data according to the data vault 2.0 modelling principles,
is one of the best examples. | scraping/output/-2606520620302271656.txt | [
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] |
Instead of referring to the physical database and table name, it’s recommended
to use the “source” and “ref” syntax. Dbt automatically generates a DAG that
takes all the dependencies into account and executes all the steps in the
correct order.
Additionally, this provides exactly the kind of data lineage end-users and
data/ML engineers are looking for.
In the config files, it’s straightforward to add table and column descriptions
and data tests.
Data tests are essential because cloud data warehouses often don’t enforce
unique or foreign key constraints. And everybody knows that fighting with data
quality is, unfortunately, a reality in most data pipelines.
Do you need to document your data pipeline? Dbt uses the config files and
models to generate an easy to use and customize the documentation website.
Reference: https://docs.getdbt.com/docs/building-a-dbt-project/exposures
### Dbt in the ML6 tech stack | scraping/output/-2606520620302271656.txt | [
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Reference: https://docs.getdbt.com/docs/building-a-dbt-project/exposures
### Dbt in the ML6 tech stack
At ML6 we are using BigQuery in a lot of projects.
Dbt has been part of our tech stack for about 1,5 years. The impact has been
tremendous.
SQL-based steps in data pipelines are easier to write, complex dynamic logic
is written only once and reused multiple times.
Teams that rely on data enjoy the automatically generated docs, data lineage
and data tests.
From a technical perspective, we like the flexibility of Dbt.
It’s easy to integrate into our coding and DataOps/MLOps best practices.
The DAG can be executed fully serverless using Cloud Build, containerized
using Kubeflow or as part of an Apache Airflow/Cloud Composer DAG. | scraping/output/-2606520620302271656.txt | [
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] |
2021 might be the year of “Analytic Engineering”. We are looking forward to
the quickly expanding Dbt ecosystem. We are closely following up how Dataform
will be integrated into Google Cloud Platform because the functionality is
very similar, but the open-source community is smaller.
Keep an eye on our blog because we’ll post a review of Dbt Cloud, Dataform and
a more technical Dbt blog soon.
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Elevating excellence : accelerating each others talent | scraping/output/-4850200093856018142.txt | [
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] |
Implementing ML models' code promotion across environments using CI/CD
pipelines.
February 8, 2024
By
Miro Goettler
Placeholder tag
Elevating excellence : accelerating each others talent
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?
February 5, 2024
By
Julie Plusquin
Placeholder tag
Webinar | The AI Act in practice - preparing for the upcoming regulation
Explore the impact of the EU AI Act on businesses, focusing on the interplay
with existing regulations, cybersecurity considerations, and practical
approaches to implement Responsible AI.
January 11, 2024
By
Michiel Van Lerbeirghe
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January 11, 2024
By
Michiel Van Lerbeirghe
Placeholder tag
Elevating Your Retrieval Game: Insights from Real-world Deployments
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.
January 8, 2024
By
Sebastian Wehkamp
Placeholder tag
Unveiling Google's Gemini: a game-changer in the world of AI?
Explore Google's latest AI model, Gemini, challenging GPT4.
December 7, 2023
By
Matthias Feys
Placeholder tag
The impact of multimodality in the landscape of Large Language Models
Since the recent introduction of GPT-4 Vision, there has been a growing
interest in multimodality. Read what does it mean for you.
November 22, 2023
By
Pauline Nissen
Placeholder tag
Unlocking the Secrets of Life: AI Protein Models Demystified
A brief overview of what proteins are, their characteristics and the
applications of protein engineering. | scraping/output/-4850200093856018142.txt | [
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Webinar | Generative AI for corporate use - Fine tuning foundation models | scraping/output/-4850200093856018142.txt | [
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October 10, 2023
By
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Webinar | Generative AI for corporate use - Fine tuning foundation models
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.
October 5, 2023
By
Placeholder tag
The EU Data Governance Act (DGA): an engine for innovation
Discover the DGA's impact on data sharing, privacy, and innovation, and how
ML6 solutions align with its objectives.
October 2, 2023
By
Michiel Van Lerbeirghe
Placeholder tag
Get Started with Machine Learning Faster Using Amazon SageMaker JumpStart
Unlock Machine Learning with Amazon SageMaker JumpStart: A Comprehensive Guide
September 28, 2023
By
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Creating a Chatbot with Amazon Bedrock | scraping/output/-4850200093856018142.txt | [
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Unlock Machine Learning with Amazon SageMaker JumpStart: A Comprehensive Guide
September 28, 2023
By
Placeholder tag
Creating a Chatbot with Amazon Bedrock
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.
September 20, 2023
By
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AI internships at ML6: nine domains to steer your career towards
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.
September 6, 2023
By
Julie Plusquin
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September 6, 2023
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Placeholder tag
Amazon Bedrock: Building a solid foundation for Your AI Strategy
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By
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Placeholder tag
From hype to action: charting your AI course | scraping/output/-4850200093856018142.txt | [
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Understanding this behaviour and explaining why full control is unachievable
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Placeholder tag
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Placeholder tag | scraping/output/-4850200093856018142.txt | [
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] |
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AI can play a crucial role in the field of HR, but should we blindly follow? | scraping/output/-4850200093856018142.txt | [
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] |
July 24, 2023
By
Placeholder tag
AI can play a crucial role in the field of HR, but should we blindly follow?
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.
July 20, 2023
By
Julie Plusquin
Placeholder tag
Smart energy: how to make your home battery smart and save money?
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.
July 16, 2023
By
Juta Staes
Placeholder tag
Harnessing the Power of Foundation Models for your Business with LoRA
Harness large foundation models affordably with LoRA technique for business
applications.
July 13, 2023
By
Placeholder tag
Predicting enzyme heat stability during Christmas break | scraping/output/-4850200093856018142.txt | [
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] |
Harness large foundation models affordably with LoRA technique for business
applications.
July 13, 2023
By
Placeholder tag
Predicting enzyme heat stability during Christmas break
ML6 tackles enzyme stability prediction in Kaggle competition, pushing
boundaries in life sciences.
July 10, 2023
By
Pieter Coussement
Placeholder tag
AI image generation without copyright infringement
AI image generation without copyright infringement using a Creative Commons
dataset.
June 29, 2023
By
Philippe Moussali
Placeholder tag
Battle of the Clouds: GCP vs Azure
Comparing GCP and Azure from a developer's perspective.
June 26, 2023
By
Michiel De Koninck
Placeholder tag
PEFT (Parameter-Efficient Fine-Tuning)
Different methods available for fine-tuning Large Language Models (LLMs) and
when to use them.
June 19, 2023
By
Placeholder tag
Developing AI Systems in the Foundation Model Age : An intro
Introduction to Foundation Models and choosing the right one.
June 7, 2023
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Recording of the webinar on Generative AI for corporate use: How to get
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Placeholder tag
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By | scraping/output/-4850200093856018142.txt | [
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In-Line Quality Inspection & Process Steering
Placeholder tag
Open source catching up to OpenAI
Exploring Recent Developments in Open Source LLMs and Their Implications.
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By
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The Compliance-Friendly Guide to Using ChatGPT and other GPT models
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Generative AI
Placeholder tag
Doubling down on learning and innovation
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Placeholder tag
Low Rank Adaptation: A Technical Deep Dive | scraping/output/-4850200093856018142.txt | [
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