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People
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Corporate
People
Structured Data
Chat GPT
Sustainability
Voice & Sound
Front-End Development
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Responsible/ Ethical AI
Infrastructure
Hardware & sensors
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Accelerating businesses with AI technology & experts
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MLOps
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Domains of exp... | scraping/output/7371013274892921836.txt | [
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Machine learning opened up new ways of solving technical... | scraping/output/7371013274892921836.txt | [
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* Why and when Hybrid AI is relevant for your situation.
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Infrastructure
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Cu... | 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 record... | 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 ... | scraping/output/-4005684865848025300.txt | [
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May 28, 2021
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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 inc... | 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... | 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 cre... | 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 engineeri... | 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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Contact us to turn your structured data into valuable insights that help you
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Structured Data
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In this blog, we will uncover some pressing challen... | 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
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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
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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 m... | scraping/output/-4422453358527678687.txt | [
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