Bridging the Artificial Intelligence Skills Gap in the Machine Manufacturing Industry
Figure 3: The cyclic nature of AI/ML projects (as presented in DIMECC’s Machine Learning Academy).
One of the differentiating factors of MLA is
that the participants are expected to plan
and specify a real Machine Learning
project. The course modules are arranged
in such a way that their content follows the
flow of a typical ML project (see Figure 3).
The course arrangements also provide the
participants with several opportunities to
discuss their projects with lecturers and
the other students and share and compare
their approaches.
In order to successfully complete the
project assignment, the participants also
need to get contributions from various
internal stakeholders, such as business and
process owners, technology developers
and product managers. As topics related to
the course project are introduced and
addressed throughout the course, the
participants are encouraged to engage
with these stakeholders and get their
commitment to the new approach. The
aim is that at the end of the course, each
participant has a project specification
which key stakeholders are already
familiar with and which is detailed enough
for starting an in-house development
project or sourcing it from an external
supplier. In the spirit of co-creation, each
participant presents her/his course project
in the last module at an appropriate level
of detail.
Although the participants of the first MLA
course came mainly from R&D, their
project topics covered a wide variety of
companies’ internal functions, such as
finance (smart cash forecasting, customer
risk analysis), sales (pricing and tool,
automated
offer
generation),
manufacturing (intelligent scheduling,
process control for quality optimization),
customer care (predictive and preventive
maintenance) and human resources
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