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Digital twins rely on combining data from different sources, including
sensors, ERP systems, and other manufacturing software. The data must be
precise, consistent, and complete.
Model creation
To create a digital twin, you need an accurate model of the physical system,
which includes all relevant machines, materials, and processes. The model must
predict performance and identify bottlenecks to be successful.
Performance prediction
The digital twin should predict the production system's performance based on
various factors, including demand, raw materials, equipment availability, and
maintenance schedules, using multiple performance metrics.
Optimization
The digital twin should be able to optimize the production process by
identifying the most efficient use of resources, reducing waste, and improving
the quality of output.
Intuitive interface/dashboard
Developing an intuitive dashboard for users is important since it's often
their only way of interacting with the solution. | scraping/output/-1381642931706136732.txt | [
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Intuitive interface/dashboard
Developing an intuitive dashboard for users is important since it's often
their only way of interacting with the solution.
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Message | scraping/output/-1381642931706136732.txt | [
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As a project manager you make sure that the goals and outcomes of your project
are closely aligned with the needs and wishes of the client while monitoring
resources, timelines and technological and other requirements. You will work
with teams of highly-talented machine learning engineers delivering cutting-
edge AI solutions.
Some of your responsibilities: | scraping/output/-3687198579817695165.txt | [
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* Identify requirements with clients and stakeholders using ideation and prioritization techniques (e.g. interviews, workshops, refinements, …)
* Translate client’s requirements and preferences into tangible goals for the technical team
* Create task backlog and planning
* Check deliverables and follow up resource planning, timelines and budgets
* Set up and coordinate meetings with stakeholders and technical experts
* Safeguard user, technical, legal, ethical, security and other requirements
* Effectively pitch solutions, present results - adjust complex technical messaging to the audience at the customer
* Probe clients for other opportunities
* Flexibly interact with client’s ways of working, agile tooling
* Create project documentation
* Facilitate post project meetings to establish project takeaways
* Coach squad members on projects and in their goals
* Enable teamwork and group cohesion | scraping/output/-3687198579817695165.txt | [
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* Facilitate post project meetings to establish project takeaways
* Coach squad members on projects and in their goals
* Enable teamwork and group cohesion
* Ensure clear responsibilities and quality of delivery on projects | scraping/output/-3687198579817695165.txt | [
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## Why you?
* Excellent verbal and written communication in English and German (Berlin Office) or English and Dutch (Amsterdam Office)
* Master’s degree or similar required through relevant experience
* min. 5 years of experience as a project manager in IT, strong track record in managing data-projects
* Experience with agile project management methodologies and tools
* Ability to understand and refine client requirements
* Ability to hold team members accountable for goals, and take responsibility for the work
* Ability to plan, schedule, coordinate, and organize an interdisciplinary team
* Familiarity with data processing, machine learning is a strong plus
* Flexible, curious and willing to embrace change for the better
##
Why ML6? | scraping/output/-3687198579817695165.txt | [
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##
Why ML6?
Looking for a dynamic environment where you can create real business impact
with AI? Look no further. Don’t expect a big corporate organisation with pre-
defined jobroles and plans for the coming years. Expect the unexpected.
Growth, change and impact. Expect to work for the most innovative and biggest
companies across countries. Expect exponential personal growth in your career.
We provide an attractive salary package for your expertise and an optimised
benefits program where we stay true to our caring and learning goals. In a
nutshell, we've built a place where we truly love working, we think you will
too.
Scroll down to find out more about why others choose ML6! 👇
Department
Advisory & Delivery unit 💻
Locations
Office Berlin 📍, Office Amsterdam 📍, Office Ghent 📍
## Why others choose ML6 🤝
* ### Create intelligence with a lasting positive impact | scraping/output/-3687198579817695165.txt | [
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] |
Advisory & Delivery unit 💻
Locations
Office Berlin 📍, Office Amsterdam 📍, Office Ghent 📍
## Why others choose ML6 🤝
* ### Create intelligence with a lasting positive impact
As a leader in AI, we work hard to ensure that AI is designed, developed and
deployed to create positive impact. We take our responsibility to build secure
and sustainable technology seriously and have integrated ethics within our way
of working. This shows in every client case and do good projects we do.
* ### Innovate and share knowledge
Learning is part of our DNA. Whether it's through our team's expertise,
creating spin-offs or the latest tech breakthrough, our culture will empower
you to deliver top-notch AI solutions to our customers. We organise chapter
conferences, guest lectures, meet ups, dedicate time to research, blogposts, a
personal training catalog and so much more.
* ### We achieve more together | scraping/output/-3687198579817695165.txt | [
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* ### We achieve more together
Expect to build lasting relationships that extend beyond the office walls.
Thanks to our collaborative and diverse culture, we achieve the extraordinary.
From our ML6 agents to our ML6 Maffia, we’ve created a safe environment where
mutual trust and open feedback is cherished. To foster our interpersonal
connections, we love to see each other's pretty faces at the office.
* ### Fast growing and international
We're gearing up for our next growth milestone. Don’t expect a big corporate
organisation with pre-defined jobroles and plans for the coming years. Expect
the unexpected. Growth, change and impact. Expect to work for the most
innovative and biggest companies across countries. Expect exponential personal
growth in your career.
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* ### Byte size Benefits
Last but not least, we provide an attractive salary package for your expertise
and an optimised benefits program where we stay true to our caring and
learning goals. In a nutshell, we've built a place where we truly love
working, we think you will too.
📽️ Working as a Project Manager
00:00
% buffered
## FAQ
* ### Can I work remotely at ML6?
There is a reason why we don’t work (fully) remotely at ML6 and why we love
being present at our offices so often. At ML6, our culture is based on
interpersonal connections and our strength lies in our personal collaboration.
With our clients, and with each other. We work from home about 2 days a week.
Our offices are situated in Berlin, Amsterdam and Ghent.
* ### What to expect when joining ML6? | scraping/output/-3687198579817695165.txt | [
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* ### What to expect when joining ML6?
Don’t expect a big corporate organisation with pre-defined jobroles and a
defined plan for the coming years. Expect the unexpected. Growth, change and
impact. Learn from engaged experts with a like minded passion for technology.
Expect to work for the most innovative and biggest companies across countries.
We are all T-shaped consultants working on a shared goal.
* ### Am I a fit for the company?
We are an AI Consulting company on a mission. Guiding the AI revolution
towards positive impact. You can become part of this mission. We are always on
the look for business minded people with a consultative approach. We dedicate
great effort in building a caring culture full of learning opportunities that
accelerates us to accomplish our main purpose. You can be in the driver’s seat
of your career. You can be the next ML6 agent to create value for our
customers and company.
Are you up for the challenge? | scraping/output/-3687198579817695165.txt | [
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Are you up for the challenge?
* ### What does the application process look like?
It depends on the role that you're applying for. For our technical roles, we
always start with a technical matching (ex. coding challenge) and get to know
interview. Every role at ML6 receives a presentation challenge where we will
evaluate how you can contribute to our innovative projects by demonstrating
your skills & creativity. As you approach the finish line, we schedule a final
interview (physically) to reflect on your dream job at ML6.
* ### How is ML6 structured?
We work in Units and with Unit Coaches. For more information about our
structure, we recommend you to watch this video.
* ### What are examples of projects that ML6 worked on? | scraping/output/-3687198579817695165.txt | [
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We work in Units and with Unit Coaches. For more information about our
structure, we recommend you to watch this video.
* ### What are examples of projects that ML6 worked on?
At ML6, we boost and increase revenue growth for other companies, by
implementing AI solutions. We do this for a broad range of industries. From
manufacturing, to healthcare or media and entertainment industries (and lots
more!).
You can find some of our client cases and testimonials here.
* ### I would love to meet up with someone from ML6
You can find all of our upcoming meet ups and events here. If you would like
to schedule a first get-to-know, we recommend you to apply officially via our
jobsite where we will follow up with you soon.
* ### What about the ethical aspect of AI?
At ML6 we take our responsibility to build secure, reliable and sustainable
technology seriously and integrate ethics within our way of working. We have
our own Ethical Advisory board to discuss sensitive projects. | scraping/output/-3687198579817695165.txt | [
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* ### Why is ML6 called ML6?
ML stands for our main expertise, Machine Learning. Next to that, we don't
take ourselves always very seriously. Our people are 'ML6 agents', wink to
MI6. Seen the James Bond movies? Our meeting rooms for example are named after
them. After all, we can be a bit geeky at times ;-)
* ### What does a typical AI project look like?
This really depends on the scope of the project. A project can run for a
couple of months, to even years. Our ML6ies work on projects together with a
team of colleagues. This team can have different skills like data engineering,
software, machine learning and project management to deliver top notch service
to our clients. They sometimes work on different projects simultaneously or
combine project work with an internal project or research topic.
Contact Julie Plusquin Talent Partner – Talent & Culture 🫶
## Colleagues
Jens Bontinck
Head Advisory & Delivery
## Join us
* Frontend Engineer | scraping/output/-3687198579817695165.txt | [
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Contact Julie Plusquin Talent Partner – Talent & Culture 🫶
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## Join us
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## About ML6 | scraping/output/-3687198579817695165.txt | [
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* Data Engineering Consultant
Advisory & Delivery unit 💻 * Multiple locations
More jobs
Office Berlin 📍 Office Amsterdam 📍 Office Ghent 📍
## 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. | scraping/output/-3687198579817695165.txt | [
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health, tax and fraud.
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# Empowering nonprofits with AI Solutions | scraping/output/-315247701017859334.txt | [
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Responsible AI
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# Empowering nonprofits with AI Solutions
At ML6, we believe in the power of AI to drive positive change and make a
lasting impact on the world. Our ML6 for Good program is a testament to our
commitment to using cutting-edge technology for the greater good. We partner
with nonprofit organizations to accelerate their mission-critical initiatives
by providing them with access to our AI expertise and solutions. Here's why
"doing good" is at the heart of what we do.
Why Do GoodOur impact so farContact our team
## Why Do Good matters to ML6
"Doing good" is one of our core values at ML6. As a leading company in the
field of artificial intelligence, we recognize our responsibility to harness
AI's potential for positive, long-term change.
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With the ML6 for Good program, we extend our commitment to the nonprofit
sector by offering our services "pro bono." This enables us to utilize our
skills and tools to improve the world and the environment by granting
nonprofits access to the same advanced AI solutions that businesses benefit
from.
## Our impact so far
Our dedication to "doing good" has led to meaningful collaborations with
nonprofit organizations on several impactful projects. Here are a few examples
of the work we've done in the past.
### IDA Foundation
We partnered with IDA Foundation on three distinct use-cases, including
"Forecasting of emergency health kits," "Pallet packaging optimization," and
"Emergency event detection" based on social web data. These projects aimed to
create data-driven approaches to anticipate disasters and optimize the
transportation of critical supplies for countries in need.
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This is some text inside of a div block.
### AROP VZW
We assisted Arop VZW by developing an initial version of an automatic visual
quality inspection workflow for their sheltered workshops. This tool aids
workers in their daily activities, enhancing the quality and efficiency of
their work.
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This is some text inside of a div block.
### Fruitpunch.ai and Reef.support
In collaboration with Fruitpunch.ai and Reef.support, we've been working on a
visual detection tool with image segmentation capabilities. This tool is
designed to identify different types of corals and detect coral bleaching,
contributing to raising awareness about the preservation of these vital
ecosystems.
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This is some text inside of a div block.
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This is some text inside of a div block.
### Beeodiversity
One ambitious project we're working on is the "Pesticide Predictor." We ask
the question: Can we predict the presence of certain pesticides based on the
kind of agricultural land surrounding a measuring point? By applying machine
learning algorithms to the data collected from various agricultural regions,
we aim to develop a tool that can alert farmers and environmental agencies to
potential pesticide contamination risks. This innovative approach combines
technology and nature to safeguard our environment, promoting sustainable
agriculture and a healthier planet for all.
This is some text inside of a div block.
This is some text inside of a div block.
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### Plan International
By gaining insights into the donor journey, efficiency and effectiveness of
fundraising could be enhanced. With insights gained from a data-driven
approach, the ultimate goal is to enable fundraisers to take better and more
personal care of existing donors based on their needs, while spending less
resources and funds in the process.
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This is some text inside of a div block.
# Contact our team
Our ML6 for Good team is led by Rosemarijn Dumont and Birgit Mullie. They're
backed by a passionate group of ML6 employees eager to assist nonprofits.
Together, this dedicated team collaborates with nonprofits, ensuring AI is a
force for positive change.
Name *
Company *
Email *
Message
We need your details to contact you. Your data will be processed according to
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Copyright 2024
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Front-End Development | scraping/output/9148560787307000418.txt | [
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Another day, another change in the world of generative AI.
At this point, even a blind person can see that the generative AI hype train
has well and truly left the station and Google has officially jumped aboard.
It is no secret that Microsoft has thrown the first punch in the fight for
generative AI with its investment in OpenAI (and thus ChatGPT) but Google has
decided to come out swinging.
Will Google pull a Tyson Fury on us and rise from the canvas to win the fight,
retaining its title as the go-to cloud for AI/ML? Let’s find out! 🔎
### What does the Google generative AI landscape look like?
At Google I/O 2023, generative AI was — to no one’s surprise — one of the main
talking points. Big announcements were made and new services went live (in
preview).
Let’s take a closer look at what they came up with.
* For users | scraping/output/-5229824330364300756.txt | [
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* For users
Google announced Duet AI which is an AI chatbot assistant (one might even call
it a copilot) that is planned to be implemented in basically anything Google —
from Workspace to GCP. Google search will get a similar feature but doesn’t
fall under Duet.
All in all nothing too world-shocking in terms of ideas — especially
considering the moves Microsoft is making (e.g., Office 365 Copilot, Bing
Chat, ...) — but they are practicing what they preach.
* For builders
The big one here is Generative AI App Builder which is Google’s fast and easy
managed solution to enterprise knowledge discoverability.
A LLM-powered chat interface connected to internal enterprise documents is
such a valuable and general use case. Think about a chat interface where you
can ask questions about information in technical manuals, legal documents,
etc. | scraping/output/-5229824330364300756.txt | [
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] |
Turning this into a standardized offering is the textbook definition of a no-
brainer. The ease of development looks very impressive.
Turns out these Google folks know what they’re doing when it comes to looking
for information. Wonder if they have done this before?
For more on the underlying Retrieval-Augmented Generation (RAG) technology
behind Generative AI App Builder, click here.
* For tuners
There is also a solution for people who want to dig a level deeper and
interact and tune the underlying ML models directly. Enter Generative AI
Studio and Model Garden which are both in Vertex AI.
Model Garden is the place where you can browse through the available models to
find the one that fits your use case.
Overview of model cards available on Vertex Model Garden
Generative AI Studio is the place where you can interact with the models and
tune them to meet your use case and/or data. | scraping/output/-5229824330364300756.txt | [
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] |
Overview of model cards available on Vertex Model Garden
Generative AI Studio is the place where you can interact with the models and
tune them to meet your use case and/or data.
Security and efficiency stand out: when you tune a model, you only train a
small adapter model whereas the actual foundation model stays frozen.
Both data and adapter model never leave your cloud tenant!
Note: at the time of writing none of the above has public access except for
Generative AI Studio and Model Garden which is in public preview.
### The stand-outs
In general, we notice a clear convergence in the cloud generative AI offerings
towards the same set of ideas.
Great minds think alike but clearly cloud providers do too.
That said, Google’s genAI stack is by no means a copy and there are some
definite differences. Let’s take a look at our first impressions:
* One size does not fit all | scraping/output/-5229824330364300756.txt | [
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] |
That said, Google’s genAI stack is by no means a copy and there are some
definite differences. Let’s take a look at our first impressions:
* One size does not fit all
With PaLM 2, Google accentuated the wide range of sizes it comes in: Gecko,
Otter, Bison and Unicorn. Since only Bison is available in preview (at time of
writing), it is too early to compare but this versatility is definitely an
interesting avenue with Gecko even poised to be able to run on a mobile
device.
* Promise of fine-tuning
The few-shot and fine-tuning behavior is looking very promising — even with as
little as 5–10 labeled examples. We notice significant improvements,
especially for niche situations such as the legal or medical domains.
* Safety first | scraping/output/-5229824330364300756.txt | [
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* Safety first
It is no secret that generative models are prone to the odd ethical hiccup if
let completely loose. Therefore Google is focussing heavily on putting in
place guardrails that should inhibit any unethical input from coming in and
any unethical output from coming out.
To this end, Google uses so-called safetyAttributes that quantify how toxic,
hateful, insulting, etc. it predicts a certain in-/output to be.
This is a big step towards peace-of-mind that a company who uses generative AI
won’t end up making headlines like Microsoft Tay.
### What can we expect?
Of course it is still early days. What can we expect going forward?
* Europe compliance
This is a big one: at the time of writing there is no Google generative AI
offering where all data stays within Europe!
For sensitive use cases, this is of course a BIG no-no in the eyes of GDPR.
Obviously Google is more than aware of this and will be working overtime to
make this possible in the (near) future. | scraping/output/-5229824330364300756.txt | [
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Obviously Google is more than aware of this and will be working overtime to
make this possible in the (near) future.
* To churn or not to churn
We already covered the convergence in offerings by the major players. Whether
people will return from e.g., Bing Chat back to Google search after Google
catches up with its generative AI efforts will be the burning question on any
AI enthusiast’s mind.
* Better language coverage
As always, advances in NLP are always English-centered at first. Generative AI
Studio is no different as it currently only supports English.
However, PaLM 2 is highly multilingual so we assume this limited support is a
more a due to the bias/toxicity/… guardrails Google has put in place rather
than the capabilities of the underlying model itself.
* Multimodality
Although Google has image, text and speech generative AI offerings, there is
no multimodal generative AI yet. | scraping/output/-5229824330364300756.txt | [
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* Multimodality
Although Google has image, text and speech generative AI offerings, there is
no multimodal generative AI yet.
Considering multimodality is a booming trend, it is not difficult to envision
a world where Google releases multimodal offerings in the near future.
* GenAI-backed ML APIs
Google is practicing what it preaches and we fully expect this to extend to
its suite of ML APIs. Offerings such as Google Contact Center AI and
Dialogflow seem like obvious candidates to be backed by LLMs.
Given the impressive fine-tuning performance, we are especially excited by
domain-specific models such as MED-PaLM2 and if/how these will be
commoditized.
### Conclusion
Things are heating up in the generative AI landscape. OpenAI has hit Google
where it hurts (using their own technology no less) but they have come out
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] |
### Conclusion
Things are heating up in the generative AI landscape. OpenAI has hit Google
where it hurts (using their own technology no less) but they have come out
swinging.
Google released services that are largely in line with its competitors and it
is practicing what it preaches in its own products (e.g., Google search,
Workspace, etc.).
As far as first impressions go, it looks like generative AI on Google will
become competitive with Azure and AWS.
The big question on everyone’s mind is whether Microsoft can convert its shock
upset into generative AI dominance or whether Google will retain its title as
the AI champion.
The jury is still out on that one.
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Sustainability | scraping/output/-5229824330364300756.txt | [
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] |
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March Real Estate
# Building a recommendation engine for March Real Estate
## Impact
Since integrating the proactive recommendation tool, the number of leads from
the matching engine skyrocketed to almost 7x the number of new contacts per
day. 45% of March leads are now realized using the March Matching Engine. | scraping/output/-4652357889146377325.txt | [
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By using a data-driven approach, March also gets a high response rate from
companies. Four weeks after the initial contact 70 percent responded, and 10
percent of the companies contacted indicated that they are actually looking
for a new location at that moment.
## Intro to the customer
The Belgian startup March is a fast-growing commercial property company
specialised in matching companies looking to rent or buy commercial real
estate with developers and owners. In the generally conservative real estate
industry, March differentiates itself as an innovative proptech player which
has the human element at heart. At its core, the company uses data
intelligence to generate high-impact leads, thereby freeing the time of its
real estate agents to focus on the customer.
## Challenge | scraping/output/-4652357889146377325.txt | [
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## Challenge
A lot of factors influence the location decision of business space for
different companies in various industries. March’s vision was to use all these
variables in a fast and efficient way for targeted prospecting in order to
find the optimal match between companies and business space owners. That is
why they got in touch with ML6.
“45% of our leads are now coming from the March Matching Engine. That is very
much in line with our ambition to be twice as efficient in our industry than
our competitors. We are on the right track and can continue to work on making
our algorithm even more accurate by adding more features and further fine-
tuning the model.”
Co-founder & CEO March
By
March - Philippe Meire - Case Quote
## Solution | scraping/output/-4652357889146377325.txt | [
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] |
Co-founder & CEO March
By
March - Philippe Meire - Case Quote
## Solution
March, together with ML6 and experience design studio Bothrs, developed an
internal online tool that makes it easy to determine high-potential prospects
for a particular property, based on their suitability for that location as
determined by machine learning algorithms. The March Companions can leverage
personalised information to approach these leads, allowing March to pivot from
a reactive to a proactive approach.
The March Matching Engine starts from a large collection of private and public
data. More than 50 features analyse the location, the building and the parcel
of each property in Belgium. This allows employees to generate reports for
potential clients, show them related property scores and bring all valuable
factors together in a single, convenient overview. In addition, the tool has a
few other features, such as personal and auto-generated offers.
By
## Results | scraping/output/-4652357889146377325.txt | [
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By
## Results
Since integrating the proactive recommendation tool, the number of leads from
the matching engine skyrocketed to almost 7x the number of new contacts per
day. 45% of March leads are now realized using the March Matching Engine. By
using a data-driven approach, March also gets a high response rate from
companies. Four weeks after the initial contact 70 percent responded, and 10
percent of the companies contacted indicated that they are actually looking
for a new location at that moment.
Accelerating businesses with AI technology & experts
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References
Client casesCustomer testimonials | scraping/output/-4652357889146377325.txt | [
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# Julie Plusquin
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As a Talent Partner, Julie focuses on attracting, recruiting and retaining
talent within our organisation.
She loves analysing human behaviour and investing in personal growth. She is
convinced that the right place can be found for every talent. | scraping/output/4068675949401372701.txt | [
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Julie likes to step away from traditional recruitment methods and loves
listening to the person & their story behind the 'traditional CV'. Every story
is different and everyone gets energised by something different.
She also likes to do research about evidence-based management theories in a
business psychology context.
Are you in for a talk with her to explore the possibilities? Feel free to
reach out via Linkedin !
Visit my LinkedIn profile
Our know-how
## Related posts
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? | scraping/output/4068675949401372701.txt | [
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February 5, 2024
By
Julie Plusquin
Placeholder tag
AI internships at ML6: nine domains to steer your career towards
At the end of college years, it can be very confusing to start determining
your next steps. Are you 100% convinced about your choice of study? Do you
have doubts about how your education plays out in practice? These questions
certainly don't come as a surprise. There is a lot coming at you. For this
reason, it is very interesting to explore the field in practice. Luckily,
schools often offer that option through internships.
September 6, 2023
By
Julie Plusquin
Placeholder tag
AI can play a crucial role in the field of HR, but should we blindly follow?
Is AI only applicable within factories and large corporate organizations?
Anything but. AI will (and already is) creating impact within the Human
Resources branch as well. We speak with Julie Plusquin, Talent Partner at ML6
about what the evolution within HR may look like.
July 20, 2023
By
Julie Plusquin | scraping/output/4068675949401372701.txt | [
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] |
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Thank you! Your submission has been received! | scraping/output/4068675949401372701.txt | [
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September 6, 2022
# Triton Ensemble Model for deploying Transformers into production
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Share this post | scraping/output/1562919865548539780.txt | [
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Share this post
The blog post explains how to deploy large-scale transformer models
efficiently in production using the Triton inference server. The post
discusses the challenges associated with deploying transformer models and the
benefits of using Triton for deployment. It also describes the ensemble
modeling technique and how it can be used to improve the performance of
transformer models in production. | scraping/output/1562919865548539780.txt | [
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] |
You will learn about the Triton inference server, its benefits and how it can
be used for deploying large-scale transformer models. You will also learn
about ensemble modeling and how it can help improve the performance of
transformer models. The post includes code examples and step-by-step
instructions for deploying transformer models using Triton and ensemble
modeling. By the end of the post, you will have a good understanding of how to
deploy large-scale transformer models in production using Triton and ensemble
modeling.
The blogpost can be found on our Medium channel by clicking this link.
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] |
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FondantConnexionApache BeamHugging FaceGithub
## Fondant
Production-ready data processing made easy and shareable | scraping/output/2241256086645873529.txt | [
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] |
NLEN
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Open source
# A(G)I for everyone
FondantConnexionApache BeamHugging FaceGithub
## Fondant
Production-ready data processing made easy and shareable
Fondant aims to become the open-source hub for people to share and reuse data
processing components and data pipelines. Today, Fondant is being used for a
multitude of use cases, such as multimodal RAG and multi-agent workflows. In
the future, Fondant will empower users to prepare data for AGI solutions.
Visit our website page and start testing and contributing to Fondant!
Discover Fondant
## Connexion
Learn more
Connexion is the Python web framework that enables you to build API-first. We
have been maintaining it since 2021, working towards a major 3.0 release. With
around 5 million downloads per month, it is a staple of the Python API
ecosystem.
### Connexion Docs
View more
Infrastructure
### Connexion Repository
View more
### Why we decided to help maintain connexion | scraping/output/2241256086645873529.txt | [
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] |
### Connexion Docs
View more
Infrastructure
### Connexion Repository
View more
### Why we decided to help maintain connexion
View more
## Apache Beam
Learn more
Apache Beam is a framework for unified batch and streaming pipelines, which is
mostly known as the framework underlying Google Cloud Dataflow, but can be run
on almost any distributed processing backend such as Apache Flink or Spark. We
have ported Apache Beam to Python 3, added ML inference functionality, and
created a lot of content for the community.
#### Apache Beam Blogpost
View more
#### Apache Beam AI/ML Docs
View more
#### Apache Beam Repo
View more
## Hugging face
View all
We leverage Hugging Face a lot in our solutions, but we also contribute back.
We have published models, datasets, and demos for the community. Check our
space for more.
Natural language processing
### German Toxic Comment Detection
View more
Structured Data
### Dynamic Pricing
View more
Natural language processing | scraping/output/2241256086645873529.txt | [
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] |
Natural language processing
### German Toxic Comment Detection
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Structured Data
### Dynamic Pricing
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Natural language processing
### Semantic Search Demo
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Natural language processing
### Terms & Conditions Summarizer
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Generative AI
### Logo Generator
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## GitHub
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Github is the home for all our open source code projects. Check our Github
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Natural language processing
### Explainable transformers using SHAP
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Natural language processing
### Neural Keyword Extraction
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Natural language processing
### Text Augmentation using large-scale LMs and prompt engineering
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Natural language processing
### Gender debaising of datasets using CDA
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Natural language processing
### GPT2 Quantization using ONNXRuntime
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Structured Data
### Vertex AI TensorBoard alternative for smaller budgets
View more | scraping/output/2241256086645873529.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/-8846698550655468162.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/-8846698550655468162.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/-8846698550655468162.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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] |
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Rob Vandenberghe | scraping/output/-8846698550655468162.txt | [
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### Visualising Dynamic Pricing modelling to build insights
The nice thing about Dynamic Pricing is that it offers a level of
explainability. The demo below shows an experiment lasting two weeks in which
we are training a dynamic pricing model that is interacting with a fictitious
market.
For this experiment, 100 data points (=potential clients being shown a price)
were generated daily, based on the prices that were chosen by the model. The
model is then retrained overnight on the daily that came in the day prior.
In a matter of a few days, the suggested prices start revolving around the
optimal price which is indicated by the vertical dashed line - while at the
same time exploring nearby prices still.
Video 1. A demo indicating the visual and explainable nature of Dynamic
Pricing models. | scraping/output/-6435728918303101593.txt | [
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Video 1. A demo indicating the visual and explainable nature of Dynamic
Pricing models.
Want to learn more? Please have a look at the blogpost which explains the
characteristics of Dynamic Pricing modelling from a business perspective.
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# Julien Schuermans
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Julien has a background in Electrical Engineering and Computer Science and
obtained an advanced Masters’ degree in Artificial Intelligence. At ML6 he has
worked for clients in a wide variety of industries: manufacturing, real
estate, network security, and law enforcement. | scraping/output/-627206788474025535.txt | [
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During these projects, Julien has been responsible for the design and
implementation of end-to-end ML pipelines and powerful, scalable APIs running
24/7 in the cloud or on-premise. One of his microservice-based solutions is
handling up to 600k messages per minute! These projects really sparked his
interest in the opportunities & challenges of structured data.
As the Chapter Lead of the Structured Data research division, Julien guides a
team of experts in the domains of tabular data and time series.
His mission is twofold:
First of all, he keeps ML6 engineers up to date with the latest research and
best practices. The chapter generates, collects and distributes valuable
insights for projects dealing with topics such as anomaly detection,
forecasting or clustering. | scraping/output/-627206788474025535.txt | [
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] |
Secondly he supports the Labs and Business Development units with technical
expertise during the pre contractual phase: Julien has been involved with
spotting opportunities and drawing out the best possible solutions for
technical problems.
Visit my LinkedIn profile
Our know-how
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Large Language Model | scraping/output/-627206788474025535.txt | [
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August 30, 2022
# Potential applications of discrete diffusion models
Contributors
Bert Christiaens
Machine Learning Engineer
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### Introduction
In this blogpost, we’ll examine the applications of using VQGAN combined with
Discrete Absorbing Diffusion models from the amazing paper:
Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing
Diffusion for Fast High-Resolution Image Generation from Vector-Quantized
Codes. | scraping/output/-1575485975548286455.txt | [
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Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing
Diffusion for Fast High-Resolution Image Generation from Vector-Quantized
Codes.
If you are curious about the clever techniques that made this possible, the
paper itself and our technical blogpost will definitely be something for you!
In the technical blogpost we gave an overview of the previous SOTA generative
models, to then arrive at a new class of models, the diffusion models. We went
in depth on the architecture of the Discrete Absorbing Diffusion models. In
this blogpost, we will take a step back and look how we can leverage the
characteristics of this model for creative purposes.
### Recap technical blogpost
In the technical blogpostI we discussed two models: | scraping/output/-1575485975548286455.txt | [
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### Recap technical blogpost
In the technical blogpostI we discussed two models:
* VQGAN, a model that represents data in a 16x16 grid of discrete latent codes and is able to decode them back to image space
* the Discrete Absorbing Diffusion model, a transformer model that learns which combinations of discrete latent codes result in realistic and consistent images
In the technical blogpost we saw that a discrete diffusion model doesn’t
generate these latent codes from left-to-right, such as an autoregressive
model, but can generate them in a random order.
Out-of-order generation of latent codes
This out-of-order and bidirectional sampling allows us to use novel techniques
to edit and generate images:
* Image generation from scratch
* Conditional image generation
* Image inpainting
* Stitching images
* Generating larger images
### Image generation from scratch | scraping/output/-1575485975548286455.txt | [
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* Image generation from scratch
* Conditional image generation
* Image inpainting
* Stitching images
* Generating larger images
### Image generation from scratch
As in most generative models, we can generate images from scratch. The
diffusion model starts with an empty 1I6x16 grid, denoted by MASK tokens, and
iteratively generates tokens to fill up the grid. Once every code is
generated, the VQGAN decoder uses these generated codes to create globally
coherent images competitive with other generative models.
Faces generated from scratch by sampling from a discrete diffusion model
trained on the FFHQ dataset
Churches generated from scratch by sampling from a discrete diffusion model
trained on the LSUN churches dataset
### Conditional image generation
Now let’s talk about conditional image generation. This is a fancy way to say
that we choose a part of the image, to steer the generation of the rest of the
image. | scraping/output/-1575485975548286455.txt | [
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Now let’s talk about conditional image generation. This is a fancy way to say
that we choose a part of the image, to steer the generation of the rest of the
image.
With the existing autoregressive models, it is possible to use conditional
image generation based on a partial image. The idea here is to give the model
the top part of an image as context and then further complete the content by
predicting the next pixels one-by-one in a top-to-bottom, left-to-right
fashion. Instead of predicting pixels, we could also predict discrete codes in
a unidirectional manner, which is an idea that was explored in the paper:
Taming transformers for high-resolution image synthesis.
Impressive, right? However, it is only possible to condition on the top part
of an image, due to the unidirectional nature of autoregressive models.
Unidirectional prediction in autoregressive models
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Unidirectional prediction in autoregressive models
That’s where our diffusion model can do better! We can encode an image with
the VQGAN encoder, giving us a grid of latent variables. Then we choose which
parts of the image we want to keep and let the model generate everything
around it.
This is a big improvement in flexibility compared to autoregressive models
since we are no longer restricted to specific parts of an image. As a
demonstration, in the following figure we want to keep the tower in the middle
with different generated backgrounds. We keep the codes in the middle region
🏯, replace the other codes with the MASK value and ask the model to generate
multiple new images.
Left: generated image with fixed region in the middle. Right: full generated
image
As you can see, the content and structure of the middle region stays fixed for
the most part, only adapting slightly to better fit the surrounding pixels.
Feels great to tell the model what to do 😎!
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] |
### Regenerating image regions (inpainting)
Similar to the conditional image generation, we can also perform image
inpainting 🎨🖌.
Let’s suppose we generate an image and we like most of it, except for one
region that feels just not quite right. No problem, we can simply mask out the
latent codes from the grid that correspond to this unwanted region and let the
diffusion model regenerate them. To show how this works, let’s generate some
new mouths for! By regenerating these codes multiple times, we can get many
variations.
Regeneration of a region of the image while keeping the other content fixed
Very cool! We get 9 new images with exactly the same hair, eyes and background
but with completely different mouths 👄 .
### Stitching images together | scraping/output/-1575485975548286455.txt | [
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Very cool! We get 9 new images with exactly the same hair, eyes and background
but with completely different mouths 👄 .
### Stitching images together
Now the real fun can start. Due to the grid structure of the VQGANs latent
space, the codes learned by the VQGAN are highly spatially correlated to the
content of the generated images. This means that the latent codes that
correspond to the region of the eyes of a generated face will contain
information about the eyes.
Okay, but now what? Let’s take the latent codes of the 👀 of image A and codes
of the 👄 of image B. We now paste them in a grid and mask out all the other
tokens and have our diffusion model do what it’s best at.
The latent variables corresponding to the mouth and eyes are taken from the
left faces and put into an empty grid with masked tokens. The masked tokens
are predicted by the diffusion model and decoded into the face on the right.
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] |
We see that the diffusion model has nicely filled in the masked regions to
create a coherent face while staying true to the original look of the mouth
and eyes.
Comparison of the eye and mouth regions from the source (left) and generated
(right) images.
Let’s get creative and apply this idea to a model trained on churches. The
pope wants you to build a new church and particularly loves the base of the
famous Notre Dame in Paris and the tower from the magnificent Sint-Baafs
cathedral in Ghent. No problem, you can just take out the codes corresponding
to wanted regions and paste them onto an empty latent space.
All that is left is to ask the diffusion model to fill in the empty regions
and decode them with VQ-VAE and you can easily generate an endless amount of
new churches that comply with the constraints.
Left: generated image with highlighted masked region. Right: full generated
image
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] |
Left: generated image with highlighted masked region. Right: full generated
image
As you can see, the content of the towers and the base stays the same and the
model realistically fills in the rest. The pope is very happy with the results
and gives you a VIP ticket to skip the line at Saint Peter’s gates. Job well
done! If you want some crazier results, you can adjust the sampling
temperature of the diffusion model to get some more variation (while trading
off some global consistency).
### Generating larger images
The last cool application of this model is that it allows us to generate
images that are larger than the images the model was trained on. This is
accomplished by dividing the latent space of the larger image into multiple
overlapping grids that match the original 16x16 shape. At each prediction step
we compute the probabilities of new tokens and aggregate them across the
different grids.
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] |
The process of producing larger images than trained on. The latent grid is
divided into smaller grids that each calculate the probabilities of their
masked tokens. These probabilities are aggregated across the grids to get a
probability map of the original size, from which a new code can be sampled.
Afterwards, the full grid of codes is decoded by the VQGAN decoder.
This trick allows us to generate globally consistent images, even though the
model was never trained for it.
Generated images of higher resolution
### Conclusion
In this blogpost we have discussed how the bidirectional and iterative nature
of the recently emerging diffusion models, combined with the discrete
representations of VQGANs and the long-range modelling capabilities of
transformers allows us to have more control over the latent space. This
architecture produces high-quality and consistent images while adding the
ability to edit images in a conceptual discrete space. | scraping/output/-1575485975548286455.txt | [
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] |
Keep an eye out, because it’s not the last you’ll see of these diffusion
models (in fact, various amazing new papers have been released while writing
this post, such as DALL-E 2, ImageGen, Stable Diffusion…). And don’t forget to
check out the technical blogpost to learn what happens behind the scenes!🤓
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Natural language processing | scraping/output/-1575485975548286455.txt | [
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Share this post | scraping/output/6096204450617945317.txt | [
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Navigating the Parameter-Efficient Fine-tuning (PEFT) landscape can seem like
a daunting task. As AI and machine learning continue to evolve, it becomes
increasingly important to understand the different methods available for fine-
tuning Large Language Models (LLMs) and when to use them.In this overview, we
dive deep into four prominent PEFT methods: Prompt Tuning, LoRA (Low-Rank
Adaptation), Adapters, and Prefix Tuning. Each slide explains what the method
is and when it's best to use it.Remember, choosing a PEFT method is all about
aligning with your objectives. Whether you're aiming for diverse output, task-
specific attention patterns, multiple tasks on the same model, or modifying
learned representations, there's a PEFT method for you. Scroll down to expand
your knowledge on PEFT methods and make an informed decision next time you
need to fine-tune an LLM! | scraping/output/6096204450617945317.txt | [
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