For many organisations, artificial intelligence (AI), and so
machine learning (ML), is becoming an increasingly important part
of their strategic road maps. The organisations have realised the
added value that advanced analytics solutions can bring to their
company and have perhaps already hired the first data scientists to
lever the possibilities of AI.
However, if we want to ensure that we are developing machine
learning use cases that are not just becoming another
proof-of-concept (PoC) in the dreaded PoC graveyard,we need to
consider how to deploy the machine learning models after
they have been developed.
When deploying machine learning models into production, it is
crucial that we take a structured approach. We need to ensure that
the models are always representing the world they are trying to
simulate, and that the output is unbiased and relevant. As the
world is changing, so will an ML model also need to change as
models become stale and outdated if no
