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#### Blogposts
State of the LLM: Unlocking business potential with Large Language Models:
https://bit.ly/3Wqa3QL
Low Rank Adaption: A technical deep dive: https://lnkd.in/ek478dSs
#### Papers
Adapters: https://lnkd.in/epXRCzRN
LoRA: https://lnkd.in/eFGq3yZW
Prefix-Tuning: https://lnkd.in/eJ9ixFpk
Prompt Tuning: https://lnkd.in/ezB5zM8Q
#### Repos
LoRA: https://lnkd.in/exAMvMfG
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May 17, 2023
# AI Act Amendments (Joint committee vote 11/5/23)
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Share this post | scraping/output/516051411745539784.txt | [
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It has been more than two years since the first draft of the EU AI Act has
been published. Since then, we have seen technology move forward at an ever
increasing speed, with innovations such as generative AI and foundation models
gaining public attention and being applied in real-world AI solutions.
Regulators have also been moving forward, and there have been significant
evolutions of the proposed regulation. In light of the joint committee vote in
the EU parliament last Thursday (11. May 23), a short synthesis of some of
the, in our opinion, most interesting amendments in last week’s proposal:
### Prohibited & high risk applications
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] |
### Prohibited & high risk applications
The AI Act groups applications into 4 risk categories. In the latest proposal,
some additional prohibited practices have been introduced. For example,
emotion recognition will be fully banned in law enforcement, education,
workplace & border management.
The list of high risk applications has also been extended. For example,
recommender systems used by social media platforms will be classified as high
risk due to their scale and potential to influence public opinion. Obligations
for high-risk systems have also been extended, with additions such as a
fundamental rights impact assessment and information on energy consumption
within the technical documentation.
### Foundation models & Generative AI
| scraping/output/516051411745539784.txt | [
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### Foundation models & Generative AI
One of the major additions are provisions on foundation models (such as GPT or
Stable Diffusion), which, back when the first draft of the AI Act was
published in 2021, have not existed yet. AI Foundation model providers will
need to mitigate reasonable foreseeable risks, implement data governance
measures, assess bias & provide documentation. Providers of Generative AI
foundation models will additionally need to declare that an output is AI
generated, and make a summary of the use of training data protected under
copyright law publicly available.
### Open-source
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### Open-source
Unlike in the first draft from 2021, open-source is now explicitly mentioned
in the amended proposal. The requirements stated above for foundation model
providers are also applicable to open-source foundation model providers. This
has already given rise to a public debate on the implications for open-source
in Europe, if adopted. Aside from foundation models, the current proposal now
states that the regulation does not apply to open-source AI components, except
if put on the market as part of a high risk system.
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] |
After another vote in the parliament in June, this will be further discussed
and adapted in trilogues, and is expected to come into force no earlier than
2025. At the same time, we believe that companies cannot wait until the AI act
is final and in force. Those who want to play an active role in the
trustworthy and positive use of AI need to act now and start implementing
processes and practices to account for the upcoming regulation as well as for
their own values on ethical and robust AI already today.
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September 11, 2021
# How to detect small objects in (very) large images
Contributors
Maximilian Gartz
Machine Learning Engineer
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Oops! Something went wrong while submitting the form. | scraping/output/993706936005651651.txt | [
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Share this post
In this blog post, ML6 presents a solution for detecting small objects in very
large images using a combination of image tiling and object detection
algorithms. The blog post explains the challenges of conventional object
detection models on such images and introduces a method to divide the image
into smaller tiles and perform object detection on each tile individually.
The blog post also provides practical examples of implementing this approach
using the YOLOv5 object detection model and the OpenCV library. ML6 highlights
the benefits of using this method and emphasizes the importance of careful
tuning to achieve optimal performance. | scraping/output/993706936005651651.txt | [
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] |
Overall, the blog post offers a solution for detecting small objects in very
large images that is implemented through a combination of image tiling and
object detection algorithms. We provide practical examples and highlights the
importance of tuning to achieve optimal performance.
Read the full interactive blogpost on our Medium channel.
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### Introduction
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### Introduction
“Data is the fuel for AI” — While the importance of data for building
performant ML solutions is undeniable, we see in practice that the main focus
is often put on the AI model, i.e. trying out different models or tuning
hyperparameters. Some researchers have been calling to move away from this
model-centric approach, and to focus on systematically changing data to
improve performance of our solutions. In other words, taking a data-centric
approach to AI.
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In practice, we see many organisations and projects still struggling to unlock
the full potential of data. In this blogpost, we describe our view on how to
maximise the value of data to drive better performance of ML models while
minimising the cost of doing so. It goes without saying that a having a
training data set that is representative of what the model will later see in
the “real world” is of crucial importance. In this blogpost, we will focus
particularly on three other important areas — data labelling, data quality and
data augmentation.
In a second blogpost we will look at the topic of data from a different angle:
how can we unblock use cases that lack (usable) data through data
anonymisation, synthetic data or external data. More on this later!
### Data Labelling
| scraping/output/8203006516565559209.txt | [
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] |
### Data Labelling
Labelling, i.e. creating an annotated training dataset for the model to train
on, is often a crucial step when starting a Machine Learning project. This
step, and especially the quality of the labelling, can have a strong influence
on the performance of a model. | scraping/output/8203006516565559209.txt | [
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Let’s look at an example. In one of our projects on text classification, we
needed to reach an accuracy of 80% in order for the solution to have practical
business value. The first model only reached an accuracy of 65%, and we had
two options to improve on this — improving the model, or improving the labels
(the data). By trying out more complex and costly models, an accuracy of 68%
could be reached — still far off the business goal. The second option turned
out much more effective — improving the quality of the labels allowed us to
reach 87%. Why? We realized that in the first iteration the labels were
inconsistent — two different labellers only agreed in 69% of the cases on the
correct label. This of course made it impossible for the model to learn
meaningful patterns and make accurate predictions given that even people
didn’t agree on what the right labels should be.
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] |
The example above demonstrates the importance of consistent labelling to build
high performant ML models. We have collected 3 considerations to take into
account to ensure high quality, cost-effective labelling:
#### 1\. Selecting an appropriate labelling tool and/or workforce provider
There are many different labelling tools and providers available on the market
— choosing the right one for your labelling efforts is essential to ensure
quality and boost the efficiency of your labelling efforts. | scraping/output/8203006516565559209.txt | [
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The selection of your labelling tool or provider depends strongly on the use
case(s) and needs. You will need to look at the different modalities and
features a tool offers (e.g. for images & video, natural language, etc.) and
make sure they are aligned with your current and potentially future needs.
Some providers offer not only the tool, but also outsourcing of the actual
labelling activity — this can be a very cost-efficient way to label, unless
specific domain knowledge is required. Furthermore, you will need to consider
the ease of setting up the labelling environment, as well as privacy and
security of the tool. Last but not least, different pricing models can be
found on the market, from free open-source tools to commercial solutions with
e.g. pay-per-user, pay-per-annotation or monthly flat rate pricing.
We did a comparison of some open source tools for labelling structured data in
the past. The insights can be found here.
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We did a comparison of some open source tools for labelling structured data in
the past. The insights can be found here.
#### 2\. Ensuring consistency of labelling through clear guidelines
Ensuring quality and consistency is key to building a high performing ML
model. Different labellers however often have different labelling conventions,
as seen in our example above. What can help here are clear guidelines for your
labellers. These guidelines should contain instructions on how to label, as
well as “golden examples” and expected difficult or rare cases.
Also make sure to include both machine learning and domain experts from the
very start of the project and define an approach together. If possible, it is
helpful to paint the overall picture of the project to the labellers, as this
allows them to make better judgement calls or know which concerns to raise. | scraping/output/8203006516565559209.txt | [
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] |
Finally, reaching a label accuracy/consistency of 100% is typically
unattainable, either because the problem is inherently ambiguous and/or
depending on the size of the dataset it’s impossible for labellers to avoid
making mistakes. Therefore it’s essential to get an understanding of the
quality/consistency of the annotations given that these imply an upper limit
on the quality of the model. Having a label strategy that requires multiple
people to label the same data not only helps to improve the
quality/consistency, but also provides insights into the actual quality of the
labels.
#### 3\. Taking an iterative approach | scraping/output/8203006516565559209.txt | [
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#### 3\. Taking an iterative approach
While high quality is important, efficiency is too. Labelling is often a
resource-intensive process. It therefore makes sense to take an iterative
approach to labelling. Start off with 5% and review/discuss problems and
improvements. This way you can ensure that no labelling efforts are going to
waste. The ability to indicate uncertainty when labelling can help to spot
assumptions and seek additional insights from domain experts on difficult
cases. | scraping/output/8203006516565559209.txt | [
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Let’s look also at the bigger picture — you can have important efficiency
gains in later iterations by limiting additional labelling efforts to data
that will lead to the biggest improvements in model performance. Embeddings
can be used to help visualise the data, cluster similar data together and find
cases where your model has the most difficulties with. These visualisations
can lead to a better understanding of your model and data and enable you to
iteratively improve the model with more targeted labelling efforts.
Embeddings (Source: https://labelbox.com/blog/stop-labeling-data-blindly/)
##### Find more tips and tricks on Data labelling from our research:
* Effective Image Labelling for CV
* Quick Tip Timeseries Labelling (comparison of tooling)
### Data Quality
| scraping/output/8203006516565559209.txt | [
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] |
##### Find more tips and tricks on Data labelling from our research:
* Effective Image Labelling for CV
* Quick Tip Timeseries Labelling (comparison of tooling)
### Data Quality
The second topic we want to address in the context of improving our models
with data is data quality. Consistent and high quality labels are of course
part of data quality, but there are also other dimensions to take into account
— for example making sure the data being fed into a ML model does not contain
errors, missing data or outliers. Data quality has a great impact on model
performance and improving it is often a very necessary step in the ML
workflow. | scraping/output/8203006516565559209.txt | [
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Let’s look at a short example to demonstrate this. For one of our projects, we
needed to extract information from scanned text documents. OCR (optical
character recognition) is the first step in this process, however some things
usually get mixed up, such as zeros & O’s. Such a mix-up in the OCR process
often means that a subsequent model cannot detect the mixed-up inputs as a
data point anymore, diminishing the performance of the model. Improving data
quality, in this case by doing OCR correction (automatically removing common
OCR errors), led to a significant increase in performance of the model.
Example of OCR correction (Source: ML6 Hugging Face Space)
#### 1\. Understanding your data is key | scraping/output/8203006516565559209.txt | [
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] |
Example of OCR correction (Source: ML6 Hugging Face Space)
#### 1\. Understanding your data is key
At ML6, we have multiple default steps to improving data quality, depending on
the Machine Learning domain a project is in. One thing that all projects have
in common though is that a high quality data set starts with a good
understanding of the data. Data exploration is key to building up this
understanding, and is therefore often a first step when starting an ML
project.
Visualising the data is always a good starting point, from looking at the
distribution and potential anomalies within structured data, to comparing
embedding spaces for language or computer vision. Naturally, understanding
your data is not a one-off thing to do. After training a model, we need to
identify types of data that the algorithm performs poorly on, and iteratively
improve upon these difficulties.
#### 2\. Automating the process as much as possible to boost efficiency | scraping/output/8203006516565559209.txt | [
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#### 2\. Automating the process as much as possible to boost efficiency
Improving data quality is often tedious work (some say 80% of time working on
an ML project is data cleaning). Therefore, we try to automate it as much as
possible — smartly using Machine Learning or other automated techniques, to
help us be most efficient in improving data quality. | scraping/output/8203006516565559209.txt | [
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Within our ML pipelines, we want new data to automatically run through some
checks and visualisations. Some widely applicable tools that can help with
most use cases are BigQueryML with automated anomaly detection that easily
help you spot outliers in your data. TFDV for easy visualisations of your
dataset and for detecting changes over time, and finally GreatExpectations for
integrating validation rules based on business logic. We are also always
testing new frameworks and methods to keep improving and automating. A very
promising framework/approach we are currently investigating is PClean, a
domain-specific probabilistic programming language for Bayesian Data Cleaning.
##### Find more tips and tricks on Data Quality from our research here:
* Dutch OCR corrector
* TFDV for schema validation & Training-Serving Skew + Drift Detection
### Data Augmentation
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] |
* Dutch OCR corrector
* TFDV for schema validation & Training-Serving Skew + Drift Detection
### Data Augmentation
As our third topic, let’s look at Data augmentation. Data augmentation is a
set of techniques aiming to artificially increase the size of your dataset by
creating small variations on existing data points. This has two main benefits:
First, we increase the size of our dataset, resulting in more examples for our
model to train on. Secondly, the robustness of our model increases and we
decrease the risk of overfitting and bias, because the model has to learn to
ignore the transformations we have introduced and understand the underlying
data instead of memorising it.
The latter was for instance a very important part of a vacancy-candidate
matching project we did. In that project it was very important to make our
models bias free by augmenting the training data to remove as much bias as
possible, by eg. gender swapping in CVs and vacancies.
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] |
#### Developing a good data augmentation strategy
When starting an ML project, it is important to take some time to set up and
develop a good data augmentation strategy. The approach to data augmentation
differs from use case to use case, however, in general we follow three guiding
principles:
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1. Strong baselines: Machine learning is an iterative process — make changes, train, evaluate and repeat. Consequently, it is important to have a good starting point, so that iterations can be compared to that baseline to quickly figure out what works — this applies also to data augmentation.
2. Knowing your model: Some models are developed with specific data preparation in mind — disregarding this might negatively impact model performance. It is therefore important to know your model well.
3. Injecting expert knowledge where possible: The type of data augmentation you do might vary depending on the dataset and subtle differences between use cases. Expert knowledge can help to know which augmentations make sense to boost performance, and which ones might not.
##### Find more (technical) tips and tricks on Data augmentation here: | scraping/output/8203006516565559209.txt | [
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##### Find more (technical) tips and tricks on Data augmentation here:
* How to discover good data augmentation policies for ML:
* Data augmentation using few-shot prompting on large Language Models
* Transformer based data augmentation
* Gender debiasing of documents using simple CDA
With these considerations on data labelling, data quality and data
augmentation, we hope to provide some pointers on how to start maximising the
value of ML models by focusing on the data, while keeping the costs of doing
so at bay.
Keep posted for the second blog post in this series, where we will focus on
data anonymisation, synthetic data and external data!
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Things are moving fast in LLM and generative AI space. A lot of things moved
forward with the speed of light. Is this a temporary momentum we experience or
are we at day 1 of an ever expanding universe of possibilities?
For this blogpost, I would love to take you back to November 2022. Back than,
ChatGPT got released by OpenAI. Probably considered as a BIG BANG by the vast
AI community across the world.
Before, multiple initiatives (e.g. BERT, GPT2,…) were rather seen as bright
stars in the sky that made our eyes twinkle, but business value was still to
be proven at a larger scale.
Recently I was learning more about hallucination and how to mitigate it. The
goal of this blogpost is to share my view on a recent technique called
Reflexion (link to paper). Is this all we need to prevent hallucination? | scraping/output/3447542166606800182.txt | [
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The goal of this blogpost is to share my view on a recent technique called
Reflexion. Is this all we need to prevent hallucination?
#### Hello Hallucination
Hallucination can be understood as “generating nonsense”. Sometimes it puts
words, names, and ideas together that appear to make sense but actually don’t
belong together. It can occur when you’re asking a question or having a
conversation with a LLM (Large Language Model) , like GPT3.5.
Example of hallucination
Zooming out, hallucination originates in the initial purpose to which these
language models are trained (they are trained to predict the best next word in
a sentence). This is exactly where the LLM is tempted to make mistakes, and
thus generating faulty information.
While this can create funny or remarkable outputs, it doesn’t contribute to
our information industry and thus puts the trustworthiness of the LLMs at
risk.
#### Introducing Reflexion | scraping/output/3447542166606800182.txt | [
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] |
#### Introducing Reflexion
Reflexion is a recent advancement in the LLM space, and it’s a technique to
mitigate hallucination of the current generative models. The architecture
includes a feedback loop, which aims to correct common mistakes and could be
seen as a “LLM-in-the-loop” instead of an “human-in-the-loop” approach. A
powerful technique to allow self-reflection on the downstream output, if you
ask me!
“LLM-in-the-loop” instead of “human-in-the-loop” approach.
#### What does this mean exactly? | scraping/output/3447542166606800182.txt | [
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“LLM-in-the-loop” instead of “human-in-the-loop” approach.
#### What does this mean exactly?
Let’s take writing an essay as real-life example, assuming you have been at
the point you needed to write down your learnings and insights of the research
you did. Within this writing process, at some point, you need to start
writing, obviously. Whether you write chapter by chapter, or you take quick
notes first, the one thing you should do, even more than once, is re-reading
your output. You examine and ask yourself if the current sentence is
representing the message you intended to bring. If so, you are happy and you
skip to the next. If not, you iterate on that specific content piece and you
reflect which wording would convey the message even better. | scraping/output/3447542166606800182.txt | [
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This is how the concept of Reflexion works as well. If we would ask a LLM
(e.g. ChatGPT ) any prompt, we receive an output. This technique triggers
another LLM to review the output of the former model. Quite interesting and
food for thought on how this would work if we stack multiple models… — I’ll
keep that for a follow up blogpost.
Photo by Super Snapper on Unsplash
#### Impact to business applications?
This technique could improve the trustworthiness of the overall output and
would result in safer and more reliable AI systems (but probably more
expensive, given more prompts are given to a LLM?). An AI system that is able
to reflect on its output will become a more sustainable liaison to knowledge
workers and will prevent that those knowledge workers will loose trust early
on. | scraping/output/3447542166606800182.txt | [
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] |
I expect additional and similar techniques will hit the surface rather sooner
than late, and these will have a significant impact on a diverse set of use
cases like Customer support (aiming to answer your end user’s questions
correct) and Journalism (e.g. Article generation), where fake news is a topic
already.
### Conclusion — Is reflexion all you need?
Reflexion is an interesting technique aiming to mitigate the hallucination of
LLMs. This can be seen as one of the main struggles of LLM to become even more
useful. Imagine that we can believe everything it generates as an answer? It
would disrupt our way of handling information in our information industry.
Is Reflexion all you need?… If you ask me, it is a promising technique but I
do expect other techniques will hit the surface as well. I anticipate a hybrid
setup with e.g. Reflexion and advanced prompt engineering that will provide
better results over time.
## Related posts
View all
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December 15, 2022
# Welcome to the multi-language-pipeline universe
Contributors
Andres Vervaecke
Data Engineer
Jonas Boecquaert
Data Engineer
Robbe De Clercq
Data Engineer
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Thank you! Your submission has been received! | scraping/output/7781456346570369471.txt | [
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Robbe De Clercq
Data Engineer
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Share this post
This blog post introduces the concept of multi-language pipelines using Apache
Beam. It explains how to leverage I/O connectors and transforms from different
languages in your favorite language without reinventing the wheel. The post
provides a step-by-step guide with a working demo of a Python pipeline reading
and writing data from/to the Java FireStoreIO connector. It covers the
implementation of required interfaces in Java, setting up a local expansion
service, and building the Python pipeline. Although multi-language pipelines
are still in early development and have limitations, this blog post offers a
starting point for creating such pipelines.
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* Machine Learning engineer
* Data engineer
* Internships (technical)
No items found.
##
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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
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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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February 8, 2024
By
Miro Goettler
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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/-2880170531014621223.txt | [
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November 22, 2023
By
Pauline Nissen
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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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How can model providers comply with regulation concerning training data &
copyright?
October 31, 2023
By
Michiel Van Lerbeirghe
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/-2880170531014621223.txt | [
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October 31, 2023
By
Michiel Van Lerbeirghe
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.
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/-2880170531014621223.txt | [
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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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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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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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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
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
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.
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
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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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
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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
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Is AI only applicable within factories and large corporate organizations?
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The structure of FMOps to optimize your foundation model workflows.
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Introduction to Foundation Models and choosing the right one.
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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Learn how intelligent technology can optimize procurement processes with
insights from Iron Mountain, Google Cloud, and ML6.
April 21, 2021
By
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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
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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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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,
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November 24, 2022
By
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Explore the field of Hybrid AI, combining machine learning and expert rules
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October 17, 2022
By
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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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Google just released its Generative AI offering. What is it and what does it
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This is some text inside of a div block.
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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
By
Julien Schuermans
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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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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
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LoRA is revolutionizing fine-tuning for large AI models, enabling efficient
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May 17, 2023
By
Kaat Van Doren
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This is some text inside of a div block.
This is some text inside of a div block.
Explore the potential of large language models (LLMs) for businesses: models,
tooling, challenges, and applications
May 16, 2023
By
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This is some text inside of a div block.
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.
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/-2880170531014621223.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.
How we empower our agents in their own personal road to success.
March 31, 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.
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
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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/-2880170531014621223.txt | [
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] |
January 20, 2022
By
Ruwan Lambrichts
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 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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This is some text inside of a div block.
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How we tackle information management at ML6 and how our tech radar helps us do
this.
July 6, 2022
By
Georges Lorré
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This is some text inside of a div block.
This is some text inside of a div block.
A guide on how to choose the right speaker diarization framework for your use
case.
March 10, 2023
By
Philippe Moussali
This is some text inside of a div block.
This is some text inside of a div block.
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March 10, 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.
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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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
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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/-2880170531014621223.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/-2880170531014621223.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/-2880170531014621223.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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This is some text inside of a div block.
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/-2880170531014621223.txt | [
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This is some text inside of a div block.
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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
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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,
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these documents can be challenging. We'll deep dive into the three main trends
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November 24, 2021
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# Security
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Learn moreContact us | scraping/output/-8061824177189940067.txt | [
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# Security
Keep your knowledge in safe hands
Learn moreContact us
When it comes to building robust ML solutions, we always play it safe.
Cybercrime is on the rise and has become a major global problem. If cybercrime
were a country, it would have the third largest economy in the world. All the
more reason why we prioritize security, especially since we work with valuable
data sets.
In other words, protecting your data and revenue streams from theft and loss
is key to the success of your business. We take care of that by creating
secure solutions, protecting ransomware and ensuring you are compliant with
regulations.
### Build secure solutions | scraping/output/-8061824177189940067.txt | [
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] |
### Build secure solutions
We help clients manage cyber threats, data breaches, and privacy concerns
while innovating with new tech. We offer cybersecurity consulting, risk
assessments, security control implementation, and incident response planning.
### Mitigate against ransomware attacks
We help clients avoid or reduce damage from ransomware attacks, keeping their
critical business processes running and revenue flowing.
### GDPR Compliant
We keep your valuable or personal data safe from hackers and make sure you
comply with regulations such as GDPR. This protects you from financial
penalties and reputational damage.
### Typical challenges | scraping/output/-8061824177189940067.txt | [
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] |
### Typical challenges
We build our solutions on AWS, Azure, or GCP, because they provide security
features such as physical security and network protection.
The level of responsibility of the cloud provider depends on the type of
service (IaaS, PaaS, SaaS). In the case of edge deployments, we take care of
all security aspects.
We believe that communication is key when it comes to security. This is also
reflected in our security program, which makes our engineers aware of their
responsibilities through clear communication and training.
### High level outline of the solution | scraping/output/-8061824177189940067.txt | [
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] |
### High level outline of the solution
We prioritize security in all our work by following best practices and using
secure tools and templates. Our building blocks for ML challenges consider
security responsibilities and industry standards, making our codebase the
safest and easiest way to start new projects.
Need further proof of our commitment to information security? We’re happy to
share that we’re SO/IEC 27001:2017 certified, the internationally acclaimed
standard for information security management.
### Partnerships vs. doing things ourselves
We can make our solutions even more secure by collaborating with the best in
the business.
Working closely with AWS, Azure, and GCP, we take advantage of their PaaS and
SaaS solutions to minimize risks and maximize protection. Plus, we're always
in touch with their security experts to stay up-to-date on the latest tech to
keep your data safe from harm.
## Related posts
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Front-End Development | scraping/output/-8061824177189940067.txt | [
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] |
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Oops! Something went wrong while submitting the form. | scraping/output/-8061824177189940067.txt | [
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September 28, 2023
# Get Started with Machine Learning Faster Using Amazon SageMaker JumpStart
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Share this post | scraping/output/-7685331387221006903.txt | [
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Machine learning is a powerful technology with the potential to transform
businesses and industries. However, for newcomers, it can be a daunting and
time-consuming endeavour. The process of learning the fundamentals, setting up
the infrastructure, and understanding the intricacies of ML frameworks can be
overwhelming.
To address these challenges and make machine learning more accessible and
efficient for newcomers, Amazon Web Services (AWS) offers a service called
Amazon SageMaker JumpStart. SageMaker Jumpstart provides two ways to interact
with its features:
* SageMaker Jumpstart User Interface: A graphical interface for a user-friendly experience.
* SageMaker Python SDK: For more control and customization through code.
Key Findings : | scraping/output/-7685331387221006903.txt | [
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