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October 17, 2022
# Webinar | Hybrid AI: successfully combining expert knowledge with ML models
Contributors
Matthias Feys
Q / CTO
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Machine learning opened up new ways of solving technical challenges by
training models on data instead of directly implementing rules & logic. This
offers a lot of new opportunities for solving difficult problems.
However, sometimes it can also be useful to combine these machine learning
models with (expert) rules, to get the best possible outcome and leverage the
benefits of both expert knowledge as well as machine learning models.
Hybrid AI is the name of this field, and focuses on combining non-symbolic AI
(eg. machine learning), with symbolic AI (eg. expert rules). Our speakers,
Prof. Sofie Van Hoecke (PreDiCT) and Matthias Feys (ML6), will give you an
overview of this field by tackling the following topics: | scraping/output/7371013274892921836.txt | [
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### Regression & forecasting | scraping/output/-4005684865848025300.txt | [
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### Regression & forecasting
Predicting the future is hard, but with the right tools, we can forecast
trends in e.g. energy consumption or sales volume with precision. We do this
by using external data sources and taking advantage of the latest model
improvements.
### Classification & clustering
Labeling data records adds value to large data sets and automates actions. It
involves creating different labels (clustering) and assigning them to new data
(classification). This technique can be used for detecting machine failures,
sales abandonment, and clustering e-commerce users.
### Anomaly detection
We are experts in detecting anomalies in machine and process behavior. Our
strategy is to separate "abnormal" events from "normal" behavior to gain
insights into causality and explainability and use them to optimize your
systems.
### Operational research & optimization | scraping/output/-4005684865848025300.txt | [
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### Operational research & optimization
Even if a process works well, it can always be improved. This is where our
expertise comes in. We specialize in tackling complex problems such as
production planning, job scheduling, vehicle routing, box packing and more.
## Client cases
Discover how our expertise in Hardware & Sensors leads businesses to success
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The AI-Driven NGO : A data-driven approach to creating a better future for
children
By gaining insights into the donor journey, efficiency and effectiveness of
fundraising could be enhanced.
November 19, 2021
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Building a recommendation engine for March Real Estate
Ml6 built a proactive recommendation tool resulting in a 7x boost in the
number of leads from the matching engine.
May 28, 2021
By
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Public & Professional Services | scraping/output/-4005684865848025300.txt | [
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] |
May 28, 2021
By
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Public & Professional Services
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Large Language Model
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## Typical challenges
With our expertise, we can help you overcome structured data challenges in AI.
## Aligning the technical problem formulation with business problem | scraping/output/-4005684865848025300.txt | [
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Computer vision
## Typical challenges
With our expertise, we can help you overcome structured data challenges in AI.
## Aligning the technical problem formulation with business problem
Before starting machine learning (ML) model training, you need to understand
the business requirements and available data. This includes deciding whether
the problem should be approached as a regression or classification task, or
whether ranking or recommendation is required. The success of the technical
implementation is ultimately determined by meeting business expectations,
which we always strive to achieve.
## Validation is hard | scraping/output/-4005684865848025300.txt | [
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## Validation is hard
Unsupervised learning uses tools like clustering to identify data patterns,
but the results can be difficult to interpret. That’s why domain experts
during development need to make sure that the outcome is accurate. It’s also
tough to identify causal relationships between variables and labels may not be
available in situations like fraud or predicting machine failures. However,
once you overcome these challenges with our help, unsupervised learning is a
powerful tool for uncovering hidden patterns and gaining insights from data.
## Data engineering and change management | scraping/output/-4005684865848025300.txt | [
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## Data engineering and change management
Building a successful solution for structured data requires a lot of data
engineering and change management effort. Moreover, machine learning system
development can lead to hidden technical problems such as poor data quality,
model complexity and deployment challenges. To create long-term value, it’s
not enough to simply train an ML model. Validation, integration into existing
systems, ongoing monitoring and updating are essential to deliver real value
over time. We help you do just that.
## High level outline of the solution
Data collection
The first step in any structured data solution is data collection. This can be
done from a variety of sources, including internal databases, APIs, and third-
party data providers.
Data cleaning and preprocessing | scraping/output/-4005684865848025300.txt | [
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Data cleaning and preprocessing
Once data has been collected, it must be cleaned and preprocessed to make sure
that it is of high quality. This includes tasks like removing missing values,
handling outliers, and converting data types. Exploratory Data Science (EDA)
is essential.
Feature engineering
Feature engineering involves selecting and transforming the features in the
data that are most relevant to the problem at hand. This can include creating
new features, selecting important features, and scaling or normalizing
features.
Model training and selection
A machine learning model can be trained after data has been preprocessed and
features have been engineered. The selection of a specific model is based on
how well it performed on a holdout dataset.
Deployment and monitoring
Once the model has been trained and evaluated, it must be deployed into
production. This involves integrating the model into existing systems and
workflows and monitoring its performance over time. | scraping/output/-4005684865848025300.txt | [
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] |
Once the model has been trained and evaluated, it must be deployed into
production. This involves integrating the model into existing systems and
workflows and monitoring its performance over time.
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How did you hear about us? | scraping/output/-4005684865848025300.txt | [
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In this blog, we will uncover some pressing challenges within the Life Science
manufacturing industry and how breakthroughs in Machine Learning techniques
offer measurable returns.
### From batch to continuous manufacturing and pharma 4.0 | scraping/output/-4422453358527678687.txt | [
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Pharmaceutical companies are often in a race against time. Although patents
protect companies intellectual property, most of this time is spent turning an
idea into a marketable product. Traditionally medicines are produced in the
old-fashioned way by a batch process [3]. This traditional batch process has
proven to have high lead times, because after each process step the production
is stopped to test for quality assurance. Sometimes these materials are stored
in containers or shipped to other facilities before they continue in the
process [5]. Each stop increases the lead time and can cause defects and scrap
[6]. As an indication, lead times can be up to 365 days of which 228 are
dedicated to drug substance production, 75 to drug product formulation and 41
to packaging. Inventories including raw-material storage can last 250 days
[1]. Reducing these times is essential to recover the billions spent in drug | scraping/output/-4422453358527678687.txt | [
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to packaging. Inventories including raw-material storage can last 250 days
[1]. Reducing these times is essential to recover the billions spent in drug
development given the fact that there only a few years left before the patents
are expired. | scraping/output/-4422453358527678687.txt | [
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The pharmaceutical industry is often compared to the semiconductor industry
due to the high costs and the need for high throughput, volume and yield in a
clean environment with high consistency [2]. The semiconductor industry is
already quite matured when it comes to implementing industry 4.0 and this has
resulted in major advancement in technology (i.e. smaller chips with greater
capabilities in computers, phones, etc.). But what is the state of Pharma 4.0?
How is this industry moving towards the future? When compared to the
semiconductor industry there is a difference in the demanding regulations
enforced by authorities like the US FDA and the EU Commission to ensure the
quality. Changes to production in the form of digitization can result in
changes to machines, processes and even the product itself. It is these strict
regulations which are a possible cause of the conservative character of the
industry when compared to the semiconductor industry. | scraping/output/-4422453358527678687.txt | [
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