Bridging the Artificial Intelligence Skills Gap in the Machine Manufacturing Industry
covered
(i.e.
supervised
learning,
unsupervised learning and deep learning),
followed by data understanding and ethics
of AI. The sixth module helps the
participants understand how real-life
AI/ML projects are executed, and course
projects are reviewed in the last module.
Throughout the course various types of
business and technical canvases are
introduced and used as learning tools.
Their main purpose is to help the
participants understand where they need
to focus and which stakeholders they need
to engage with during the different phases
of their data science projects. For example,
the “Business Objective and Context”
canvas used in the first module directs its
users to work together with business
owners and those who fund the project
when answering questions such as “what is
the business objective [of this project]?”
and “how does it fit with our business
strategy?” Figure 2 describes how the
expertise of a cross-disciplinary project
team could be used in a typical data
science project.
Figure 2: Roles of various stakeholders and disciplines in a data science project (adapted from material presented in
DIMECC’s Machine Learning Academy, source: Futurice Ltd.).
Given MLA’s primary target group, it is not
surprising that in their feedback the
participants appreciated getting more
understanding on how ML projects can
drive and shape actual business impacts.
Also, topics related to preparing and
running practical ML projects were valued,
IIC Journal of Innovation
i.e. data preparation (collecting, cleaning,
pre-processing, filtering, analyzing, etc.)
and comparison of different ML methods.
According to one participant, “Often we do
a lot of work just to see that we are stuck
with insufficient data”.
- 28 -