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Office Ghent 📍 Office Berlin 📍 Office Amsterdam 📍
## About ML6
We guide the AI revolution towards positive impact. 🌐
Exciting developments are happening in the world of AI, offering unprecedented
opportunities for businesses to innovate and gain a competitive edge. Now more
than ever is the time to create intelligence with lasting positive impact. And
you can be a part of this!
1. We deliver end-to-end projects where we accompany organisations through the entire AI journey
2. We build bespoke hybrid ML solutions
3. We navigate and set up advisory projects
As ML6 highly values diversity, inclusion and transparent communication, we've
implemented a formal channel to report serious misconduct here.
Examples of serious misconduct (not limited to): safety of the individual
(aggression, discrimination, sexual harassment), data privacy issues, public
health, tax and fraud.
Join us
Talent & Culture 🫶 * Multiple locations
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Join us
Talent & Culture 🫶 * Multiple locations
# Junior Talent Partner
## Does the combination of people & technology sound like music to your ears?
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June 2, 2023
# Webinar | Generative AI for corporate use: How to get started with LLM
Contributors
Michiel De Koninck
Machine Learning Engineer and LLM specialist
Wolf Boone
Cloud Solutions Architect - Data & AI, Microsoft
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Wolf Boone
Cloud Solutions Architect - Data & AI, Microsoft
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ChatGPT was launched on 30 November 2022. By 4 December 2022 it had over one
million users, in January 2023 it reached over 100 million users. This numbers
allow ChatGPT to claim the title of fastest growing consumer application to
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and potential to revolutionise the way we process and interact with language.
It is fueling the awareness of companies on the way artificial intelligence
(AI) and machine learning in general will fundamentally impact the way we live
and work.
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] |
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Large Language Model
Large Language Model
Foundation Models | scraping/output/-7628868387365778338.txt | [
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## Related posts
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
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] |
AI image generation without copyright infringement
AI image generation without copyright infringement using a Creative Commons
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June 29, 2023
By
Philippe Moussali
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Who spoke when: Choosing the right speaker diarization tool
A guide on how to choose the right speaker diarization framework for your use
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March 10, 2023
By
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Large Language Model
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Services | scraping/output/-3854798385350192467.txt | [
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] |
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May 10, 2023
# How to label your way to accurate Automatic Speech Recognition (ASR)
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### Introduction | scraping/output/4438280104314944101.txt | [
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### Introduction
This blog post explores the process of labelling speech data for Automatic
Speech Recognition (ASR). ASR is the process of transcribing spoken language
into text, and it requires large amounts of unlabelled or weakly labelled
speech data to train the models. However, pre-training ASR models on this type
of data can lead to errors and biases, so labelling speech data is crucial for
accurate and robust performance. The post covers different types of speech
data, labelling methods, quality control techniques, and annotation formats
used to train ASR models. It also includes information on data formats,
speaker diarization, file duration, data augmentation, and the pros and cons
of different labelling methods. Understanding the type of speech data used and
developing an appropriate labelling methodology are essential for building a
perfect ASR model.
| scraping/output/4438280104314944101.txt | [
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### What is automatic speech recognition (ASR) and why do we need labelled
data?
In a nutshell, ASR is the process by which a machine can understand and
transcribe spoken language into text. Today, ASR models like wav2vec 2.0 and
Whisper are trained on large amounts of unlabelled or weakly labelled speech
data to learn patterns and features of spoken language. Pre-training ASR
models on this kind of data can reduce the amount of labelled data needed for
good performance, but it can also lead to errors and biases in the model.
That’s why labelling speech data is crucial in building the perfect model for
your use case. Labelled speech data enables pre-trained models to learn a more
accurate and robust representation of the spoken language is will encounter
with and decrease errors: Word Error Rate (WER) and Character Error Rate
(CER). | scraping/output/4438280104314944101.txt | [
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This blog post will look at the different types of speech data, labelling
methods, quality control techniques, and annotation formats used to train ASR
models. So, let’s get funky and dive into speech data labelling!
### Types of speech data
Speech data can come in different forms, and each type presents unique
challenges in labelling. Here are some of the common types of speech data:
* Read speech: This type of speech data is scripted, and the speaker reads from a written text. Read speech can include books, articles, or prepared speeches and is typically the easiest to label.
* Spontaneous speech: This type of speech data is natural, unscripted, and challenging to transcribe. Spontaneous speech can include interviews and public speeches.
* Conversational speech: This type of speech data involves two or more speakers interacting with each other. It can include interviews, debates, and phone conversations. | scraping/output/4438280104314944101.txt | [
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Each type of speech data presents unique challenges when it comes to
labelling. For example, spontaneous speech may require more contextual
information to be transcribed accurately, while read speech may require more
attention to detail to capture specific words or phrases. Conversational
speech may require speaker identification and turn-taking annotation to
differentiate between speakers.
Understanding the type of speech data used to develop the appropriate
labelling methodology is essential. Additionally, having a diverse set of
speech data types can improve the robustness and accuracy of ASR models.
### Data
In order to label data, you first need to make sure it’s in an appropriate
format. We won’t go into data collection in this blog post but will start with
what to do when the data is available and you’re eager to finetune your model.
There are various steps which need to be taken into account.
*If we take “format” by its most common meaning for engineers: | scraping/output/4438280104314944101.txt | [
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] |
*If we take “format” by its most common meaning for engineers:
Samples: .wav and .mp3 are the most common formats for audio samples which are
compatible with most Python audio libraries and annotation software.
Transcriptions: JSON schema is your friend. The exact format varies depending
on the annotation software used
#### Speaker Diarization
Preprocessing through speaker diarization is still necessary for inference on
models like wav2vec 2.0. Otherwise, you’ll end up with the transcript with
both speakers in one block of text.
No diarization (left), speaker diarization w/o attribution (middle), speaker
diarization & attribution (right).
Whisper does speaker diarization but not speaker attribution, meaning that
you’ll get separate blocks of speech in the output but you don’t know whom
they belong to. | scraping/output/4438280104314944101.txt | [
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Whisper does speaker diarization but not speaker attribution, meaning that
you’ll get separate blocks of speech in the output but you don’t know whom
they belong to.
Therefore it’s advised to use speaker diarization for both finetuning and
inference for wav2vec 2.0 and Whisper. If data is not collected yet, you can
ask for the different speakers to be recorded on different channels (for
example if the input source is a phone call) to outsource the diarization step
and ensure better results. If this is not possible, simple diarizer is
typically your tool of choice, but you can read our blog on speaker
diarization to help you find the optimal tool for your data.
#### File duration | scraping/output/4438280104314944101.txt | [
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#### File duration
Labellers will thank you if they have to annotate snippets of audio files
instead of an entire 5 minute phone call in one go. Whisper is finetuned on
chunks of 30s of data, wav2vec 2.0 on 10s. You can use labelled data cut into
shorter (or longer) snippets, but it will be padded (or truncated) by the
finetuning script accordingly. The optimal length for annotation is between 5s
and 8s based on our experience.
It’s advised to not clean your audio from silences or background noise (like
music or coughing), as your model also needs to learn how to deal with noise
and the absence of speech. You don’t want the model to be finetuned on
artificially clean data. What it sees now should be what it gets later.
#### Data augmentation | scraping/output/4438280104314944101.txt | [
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#### Data augmentation
To make your model more robust, you can consider augmenting your samples to
increase the diversity and quantity of your data set by artificially
generating new data from existing data. This can be particularly useful for
underrepresented accents or dialects. Techniques that can be used for speech
data augmentation are:
* Pitch shifting: for simulating different accents or emotional states.
* Tempo shifting / volume shifting / silence insertion: altering the duration of the speech recording to simulate variations in speech or to add pauses in speech).
* Noise reduction / injection: adding or removing music, white noise, background chatter, … from the recording.
* Channel distortion: adding reverberation or echo for simulating different recording environments.
These techniques can be used in isolation or combination but can decrease the
quality of your data set or teach the model unexpected behaviour.
### Labelling
#### Methods | scraping/output/4438280104314944101.txt | [
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These techniques can be used in isolation or combination but can decrease the
quality of your data set or teach the model unexpected behaviour.
### Labelling
#### Methods
Labelling speech data can be time-consuming and labour-intensive, and several
methods are available for this task. Each method has pros and cons, and the
appropriate method depends on the dataset’s size, the speech’s complexity, and
the available resources. Here are the two most common labelling methods used
for speech data that include manual labelling: | scraping/output/4438280104314944101.txt | [
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* Human transcription: This method involves human annotators listening to speech data and transcribing it manually. Manual labelling is considered the gold standard for labelling speech data as it provides high-quality annotations with high accuracy. However, it is time-consuming and expensive, particularly for large datasets.
* Computer-assisted transcription (also called model-assisted or hybrid): This method uses automatic speech recognition (ASR) tools to transcribe speech data and manually correct errors. Semi-automatic labelling can significantly reduce the time and cost of manual labelling while still ensuring high-quality annotations, which can be even more accurate than without computer assistance (see Figure below). The biggest drawback of this method is that the provided transcriptions may lead to confirmation bias for, e.g., spelling mistakes, the inclusion/exclusion of stopwords or punctuation, etc. | scraping/output/4438280104314944101.txt | [
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] |
Figure from the Whisper paper, showing the WER distributions of 25 recordings
from the Kincaid46 dataset transcribed by Whisper, 4 commercial ASR
systems(A-D), one computer-assisted human transcription service (E) and 4
human transcription services (F-I)). It shows that computer-assisted
annotations can’t only reduce time and cost but also improve accuracy.
## Metadata
Depending on your needs, you might want to annotate metadata such as:
* Topic or domain (medical, legal, …)
* Speaker identification, language/dialect, or emotion
* Level or type of background noise
* Channel or recording device
This information can provide ASR models with more contextual information to
help them better understand and transcribe speech data and help evaluate the
model’s performance on different features.
#### Annotation software | scraping/output/4438280104314944101.txt | [
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#### Annotation software
Many tools and software are available for labelling speech data, ranging from
simple text editors to dedicated annotation software. At ML6, we love Label
Studio, which offers the most customizability and elaborate features for many
domains. Among its features are the following:
* Pre-transcriptions (for computer-assisted labelling)
* Audio events (e.g. for speaker diarization)
* Customised tick boxes (e.g. for metadata)
Screenshot of an example of a labelling environment. Borrowed from the Label
Studio blog post on labelling audio data. | scraping/output/4438280104314944101.txt | [
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] |
Screenshot of an example of a labelling environment. Borrowed from the Label
Studio blog post on labelling audio data.
Annotation software like Label Studio makes labelling additional information,
like metadata, easier. It can also connect to your database to fetch the audio
snippets (and pre-transcriptions) and save the transcriptions with metadata
(as a JSON). Label Studio also allows for an iterative training loop which
uses the data annotated by humans to improve the pre-transcriptions for the
samples which still need to be labelled. A blog on labelling audio data with
Label Studio can be found here.
### Labelling guidelines
Whatever tool and method are chosen, having a labelling guide, providing
hands-on training, and analysing errors are crucial for ensuring consistency
and accuracy of the data (- no matter the domain of your data): | scraping/output/4438280104314944101.txt | [
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A labelling guide needs to explain how to use the software chosen. It provides
clear instructions on annotating different aspects of speech, such as speaker
identification, transcription, and metadata. This helps to ensure that all
annotators understand what is expected of them and that the annotations are
consistent across different annotators. | scraping/output/4438280104314944101.txt | [
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Labelling speech data requires a lot of alignment between labellers. You need
to make sure those labellers stay consistent with labelling numbers (“1”
versus “one”), capitalisation (“i drink coffee” versus “I drink coffee”),
special characters (“größer” versus “groesser”), abbreviations (“fe” versus
“f.e.” versus “for example”, or “ML6” versus “Em El Six”), punctuation,
interrupted words at the beginning or end of a sample (“” versus “good-”
versus “goodbye”), filler words (“euhm”), and many others. Including a list of
domain-specific words (= jargon) in the labelling guide is also advisable.
Furthermore, it is crucial to establish inter-rater agreement among annotators
to ensure they all apply the labelling guide consistently. Inter-rater
agreement is the agreement among annotators when labelling the same speech
data, which can be calculated with Cohen’s kappa on word- and character level
of annotations. Establishing inter-rater agreements can help identify and | scraping/output/4438280104314944101.txt | [
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data, which can be calculated with Cohen’s kappa on word- and character level
of annotations. Establishing inter-rater agreements can help identify and
address discrepancies or disagreements among annotators, improving the overall
accuracy and reliability of the labelled speech data. | scraping/output/4438280104314944101.txt | [
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] |
It is further recommended to give a hands-on workshop where the labelling
software is introduced. Some samples are labelled by all annotators to ensure
that everyone gets the same results. Questions can be addressed directly, and
a consensus for edge cases can be found and, if necessary, added or further
explained in the labelling manual.
During the labelling process, an error analysis should be conducted regularly:
collect a representative sample of the labelled speech data, identify the
types of errors or inconsistencies, categorise them, determine their frequency
and analyse the underlying causes. Your labelling guide should be revised
accordingly, and annotators should be provided additional training. In some
cases, it is necessary to switch to another labelling tool or method.
You can check out the “Data labelling” section of another blog post we wrote
here for more details.
### Conclusion | scraping/output/4438280104314944101.txt | [
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You can check out the “Data labelling” section of another blog post we wrote
here for more details.
### Conclusion
In conclusion, labelling speech data is critical in developing accurate and
robust ASR models. Labelled speech data enables pre-trained models to learn a
more accurate and robust representation of spoken language, which can reduce
spelling errors and improve the model’s overall performance. However,
labelling speech data can be time-consuming and labour-intensive, and several
methods and tools are available.
Choosing the appropriate labelling methodology and quality control measures is
essential based on the type of speech data used and the available resources.
Additionally, having a diverse set of speech data types and including metadata
information can improve the robustness and accuracy of ASR models. | scraping/output/4438280104314944101.txt | [
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] |
Finally, regular error analysis and revising the labelling guide can ensure
the consistency and accuracy of the labelled speech data. With the right
tools, methodologies, and quality control measures, you can label your way to
accurate and robust ASR models.
Stay tuned for more blog posts on ASR, such as optimising the hosting of
Whisper or the nitty gritty of finetuning it!
##### Related blog posts:
* Speaker diarization by ML6
* How to label data for computer vision projects by ML6
* Labeling audio data with Label Studio
* Finetuning wav2vec 2.0 with Hugging Face
* Finetuning Whisper with Hugging Face
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Voice & Sound | scraping/output/4438280104314944101.txt | [
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] |
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References | scraping/output/4438280104314944101.txt | [
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* Help | scraping/output/9019585196057094477.txt | [
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Oops! Something went wrong while submitting the form. | scraping/output/878102858209717161.txt | [
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There you are, newly graduated and with a degree in your pocket. You may feel
overwhelmed by the sea of job opportunities available to you. While you have
undoubtedly worked hard to earn your degree, you may now be wondering how to
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which companies are a good match can feel like a daunting task.
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Fortunately, there are some practical tips that can help. Julie Plusquin,
Talent Partner at ML6, has a wealth of experience working with students and
young graduates and can offer valuable insights. By learning how to
effectively approach the recruitment process, you can increase your chances of
landing a job that will set you up for success.
Let's break it down into more practical steps :
#### First things first
Start your job search broadly and gradually narrow it down to find the perfect
fit. Don't be too critical and be open to talking to as many companies and
connections as possible to gain a better understanding of all the
opportunities out there.
Don't be intimidated by long and detailed job postings. Take the time to read
and understand the job requirements, and create a story about what the role
really entails. | scraping/output/878102858209717161.txt | [
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Don't be intimidated by long and detailed job postings. Take the time to read
and understand the job requirements, and create a story about what the role
really entails.
It can also help to make a list of aspects that are important to you in a job
and company. This will help you narrow down your search and make more informed
decisions.
If you're looking to make an impact, grow, and learn quickly, consider working
in a start-up or scale-up environment
Do you really want to start at a particular company, but there aren't any
vacancies that match right away? Then just apply spontaneously. Personally, I
love talking spontaneously to people and seeing where they could possibly fit
within our company (now or in the future).
####
Keep it simple, but significant | scraping/output/878102858209717161.txt | [
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####
Keep it simple, but significant
Limit yourself to what is relevant in your resume and try to be original.
Interested in starting as a Data Engineer within a company? Then your student
experience as a cleaner may be less relevant to mention. When screening a
resume, the focus is often on your education, location, languages and relevant
work experience. Next to this, dare to be original and apply in a different
way. For example, consider including a short video introducing yourself or
formatting a colourful template if you are applying for a more creative job.
There are a lot of tools that could help you out.
#### Getting to know each other | scraping/output/878102858209717161.txt | [
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#### Getting to know each other
Congratulations on securing a first interview! To make the most of it,
preparation is crucial. Start by researching the company you're applying to
and take note of any questions you have about the work environment,
colleagues, and job itself. Don't be afraid to ask questions during the
interview as it shows interest and helps ensure the interview is not one-
sided. Additionally, anticipate questions you may be asked by thinking about
past experiences, such as working on a project or being part of a team. It's
also important to test your digital tools beforehand, as many first interviews
are conducted online.
#### A practical test, say what? | scraping/output/878102858209717161.txt | [
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#### A practical test, say what?
Many companies challenge their candidates’ skills. But what is the best way to
approach this? It’s important to know that there are different types of tests.
Some companies use reasoning tests, IQ tests, personality tests,... For these
tests, it is best to respond spontaneously and not to prepare. In any case,
allow enough time and a separate room to focus on these tests.
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We at ML6 rather use challenges that present a particular case study to show
you what kind of work you get to do in that role and for us to see how you
would approach it. First of all, to give you a little taste of what the job
might entail and the challenges you may face. On the other hand, this allows
us to assess which skills you already have and where you could possibly grow.
For our engineering profiles, we provide coding challenges and paper
presentations that will test both problem-solving skills, but also the ability
to interpret technical information and convey it to an audience in a
consultative manner. For our sales profiles, we will focus more on solution-
oriented sales skills. What can you do to prepare for this? Definitely read
the assignment carefully several times to make sure you understand everything.
Our challenges are generalistically formatted to ensure that you can bring
your own interpretation. Do you still have questions afterwards? Don't | scraping/output/878102858209717161.txt | [
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Our challenges are generalistically formatted to ensure that you can bring
your own interpretation. Do you still have questions afterwards? Don't
hesitate to ask them. It can certainly help to bring your presentation in a
different way than the usual slides, or to practise it with an audience a few
times up front. After your presentation, you will be asked some additional
questions. Don’t let this scare you and see it as an extra challenge and
opportunity to grow. Feel free to indicate if you do not yet know the answers
to certain questions. Nobody can be an expert from the very beginning, right? | scraping/output/878102858209717161.txt | [
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#### Feedback is the breakfast for champions
Learning during an application period is crucial. And this should happen both
ways. It is scientifically proven that people who regularly ask for feedback
or input on the tasks they are working on are considered more competent.
Daring to be vulnerable is a strength. Feel free to ask additional questions
about things that are still unclear to you and dare to ask for feedback about
your communication method, presentation, fit with the culture and so on. But
also the other way around. Give feedback to the person and company you applied
for as well. Did you find it a pleasant procedure? Could certain things have
been better? Let us know. We're both guaranteed to learn from it!
What are you waiting for ? Launch your career at ML6 via https://jobs.ml6.eu/
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Large Language Model
Large Language Model | scraping/output/878102858209717161.txt | [
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March 13, 2019
# Google’s EdgeTPU benchmarked : >10x speedup vs Intel’s Movidius
Contributors
Arne Vandendorpe
Machine Learning Engineer
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Thank you! Your submission has been received! | scraping/output/-1491928100393040130.txt | [
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The benchmark results reveal that Google's EdgeTPU Dev Board outperforms
Intel's Movidius Neural Compute Stick in terms of inference time, with a
speedup of over 10 times. The comparison was conducted using different setups,
including a laptop, the Dev Board, and the Raspberry Pi. The EdgeTPU
demonstrated faster inference times and showcased its superiority over the
Movidius Stick. Despite some usability challenges with both devices, the
EdgeTPU shows promising performance and eagerly anticipates competition from
Intel's Movidius Neural Compute Stick 2.
The blogpost can be found on our Medium channel by clicking this link.
## Related posts
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Why we decided to help maintain connexion ( Zalando )
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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. | scraping/output/-1757850497022565167.txt | [
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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
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.
Implementing ML models' code promotion across environments using CI/CD
pipelines.
February 8, 2024
By
Miro Goettler
This is some text inside of a div block.
This is some text inside of a div block.
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February 8, 2024
By
Miro Goettler
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 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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This is some text inside of a div block.
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 | scraping/output/-1757850497022565167.txt | [
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January 11, 2024
By
Michiel Van Lerbeirghe
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This is some text inside of a div block.
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
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This is some text inside of a div block.
Explore Google's latest AI model, Gemini, challenging GPT4.
December 7, 2023
By
Matthias Feys
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This is some text inside of a div block.
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
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November 22, 2023
By
Pauline Nissen
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 brief overview of what proteins are, their characteristics and the
applications of protein engineering.
November 15, 2023
By
Medha Hegde
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This is some text inside of a div block.
This is some text inside of a div block.
Optimizing OpenAI Whisper deployment in production.
November 5, 2023
By
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This is some text inside of a div block.
How can model providers comply with regulation concerning training data &
copyright?
October 31, 2023
By
Michiel Van Lerbeirghe
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This is some text inside of a div block.
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October 31, 2023
By
Michiel Van Lerbeirghe
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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.
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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This is some text inside of a div block.
Discover the DGA's impact on data sharing, privacy, and innovation, and how
ML6 solutions align with its objectives. | scraping/output/-1757850497022565167.txt | [
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This is some text inside of a div block.
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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This is some text inside of a div block.
This is some text inside of a div block.
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
This is some text inside of a div block.
This is some text inside of a div block.
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September 20, 2023
By
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This is some text inside of a div block.
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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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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September 6, 2023
By
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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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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.
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September 5, 2023
By
Sharon Grundmann
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.
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
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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.
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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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
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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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This is some text inside of a div block.
This is some text inside of a div block.
Visualising Dynamic Pricing modelling to build insights
August 3, 2023
By
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July 16, 2023
By
Juta Staes
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Harness large foundation models affordably with LoRA technique for business
applications.
July 13, 2023
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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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Unleash Astonishing Animations with Stability AI's Stable Animation SDK
June 6, 2023
By
Brent De Hauwere
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Exploring Recent Developments in Open Source LLMs and Their Implications.
May 19, 2023
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Exploring Recent Developments in Open Source LLMs and Their Implications.
May 19, 2023
By
Michiel De Koninck
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Different methods available for fine-tuning Large Language Models (LLMs) and
when to use them.
June 19, 2023
By
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AI image generation without copyright infringement using a Creative Commons
dataset.
June 29, 2023
By
Philippe Moussali
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Comparing GCP and Azure from a developer's perspective.
June 26, 2023
By
Michiel De Koninck
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Comparing GCP and Azure from a developer's perspective.
June 26, 2023
By
Michiel De Koninck
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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
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The structure of FMOps to optimize your foundation model workflows.
June 7, 2023
By
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Introduction to Foundation Models and choosing the right one.
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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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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This is some text inside of a div block.
Overcome manufacturing challenges with AI and Google Cloud. Learn from
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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
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Discover the importance of Explainable AI (XAI) in understanding machine
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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
By
Matthias Feys
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June 9, 2022
By
Matthias Feys
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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
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
Matthias Feys
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Google just released its Generative AI offering. What is it and what does it
mean for you?
June 5, 2023
By
This is some text inside of a div block.
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Google just released its Generative AI offering. What is it and what does it
mean for you?
June 5, 2023
By
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Recording of the webinar on Generative AI for corporate use: How to get
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June 2, 2023
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Generative AI as your co-pilot: specialized AI models and their impact on
design innovation.
May 25, 2023
By
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Train neural networks faster with JAX on TPUs
March 2, 2022
By
Jasper Van den Bossche
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March 2, 2022
By
Jasper Van den Bossche
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Explore the practical implementation of dynamic pricing, its effectiveness,
challenges, and the use of Thompson sampling to optimize prices.
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
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5 AI Applications to Enhance Retail Strategy Today
July 10, 2019
By
Stan Callewaert
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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
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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.
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.
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October 15, 2021
By
Jules Talloen
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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.
Improving tennis court line detection using machine learning for near real-
time predictions.
March 13, 2023
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.
Assessing Detic for object detection of thousands of classes.
May 17, 2022
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Matthias Cami
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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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Google's EdgeTPU Dev Board surpasses Intel's Movidius Stick with a >10x
speedup in inference time.
March 13, 2019
By
Arne Vandendorpe
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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
By
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December 15, 2021
By
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Open Sourcing Dutch Summarization Datasets for Transfer Learning
May 12, 2022
By
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Implementing Zero Trust Architecture for Secure Remote Work
March 24, 2020
By
Rob Vandenberghe
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Jens Bontinck shares his view on a recent technique called Reflexion
April 13, 2023
By
Jens Bontinck
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This is some text inside of a div block.
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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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Discover 3D optical acquisition methods: Stereo Vision, Structured Light, Time
of Flight, and LiDAR.
May 21, 2023
By
Francisco Pereira
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Discover the power of 3D computer vision and its impact on industries. Learn
about advantages, applications, and trends.
May 19, 2023
By
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Discover the power of 3D computer vision and its impact on industries. Learn
about advantages, applications, and trends.
May 19, 2023
By
Francisco Pereira
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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.
May 17, 2023
By
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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.
May 17, 2023
By
Kaat Van Doren
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May 17, 2023
By
Kaat Van Doren
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Explore the potential of large language models (LLMs) for businesses: models,
tooling, challenges, and applications
May 16, 2023
By
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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
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 | scraping/output/-1757850497022565167.txt | [
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May 9, 2023
By
Michiel De Koninck
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 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.
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.
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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.
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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/-1757850497022565167.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/-1757850497022565167.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/-1757850497022565167.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/-1757850497022565167.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/-1757850497022565167.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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Elevating excellence : accelerating each others talent | scraping/output/8080948775203877080.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/8080948775203877080.txt | [
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