learning is very well suited to this task
because the algorithm learns based on
behaviours, based on patterns, what
constitutes spam. Anomaly detection
in general, silent failure detections...
we are developing an advanced exper-
tise in anomaly detection because lots
of our use cases are about network
availability and anomalies.
“Historically, when equipment
misbehaved or failed, an alarm was
—
George Iskenderian,
Director, Big Data & AI
Bell Inc (Canada)“ Network operations
and performance,
failure detection: all
those concepts can
be solved with AI/ML”Recruit right
When it comes to people, recruiting
the right people was crucial to our
success. I realized I would start with
a very small team, and the quality of
that team would determine our
success.Focus on expertise
We don’t want to be mediocre. We
want to be the best at what we do,
and we don’t want to dilute our
knowledge. We don’t want someone
to be doing something else, but then
they are doing a bit of data or AI on
the side.Be flexible
My head count profile will change
from needing more engineers and
fewer data scientists to the reverse.
We don’t have pigeonholing. We
don’t have dedicated testers, project
managers, analysts. Everyone’s
either a data engineer or a data
scientist. That’s their core skill but
they can occupy various roles,
expand and grow professionally.George Iskenderian: tactics for
setting up an AI team119technologymagazine.com