The machine learning model is most certainly written in a different
language (Python: TensorFlow/PyTorch) than the language in which the
application is written (Java/C++/Ruby/Golang). This complicates
integration because the ML model must be rewritten into the
application’s native language. It is now easier to migrate ML models so
that they can be seamlessly integrated with the rest of the framework,
but it is best to construct the model in a common language to mitigate
The most obvious barrier to overcome when deploying a model is
aligning with other team members who do not have data science or machine
learning experience, such as DevOps and app developers.
Model deployment is a team effort that necessitates continuous
coordination and a shared vision of the end goal. Proper preparation
during the early stages of ML model development would assist the
MLOps/DevOps team in optimizing for deployment well in advance. It’s the
joint responsibility of all the teams working on the deployment to
ensure that the right model is deployed into production and avoid
Where it comes to the performance of machine learning models in
development, model drift is a common occurrence. Model drift occurs when
the model’s prediction accuracy falls below the recognized benchmark.
Again, it depends on the scope in which the model is used and how those
predictions are assessed, but any application will need the best version
of the back-end ML model for increased productivity and performance.
This becomes one of the primary factors for continuously monitoring the
performance of the deployed model in production.
Monitoring must be planned ahead of the actual deployment. The
framework must be in place to monitor the model’s performance on a
When it comes to machine learning deployment, there are indeed a lot
of back-and-forth tasks, and version tracking becomes an integral aspect
of the overall successful deployment process. As a result, version
control aids in tracking which model is the best, the various file
dependencies, and the data resource pointers. Version management can be
done in a variety of ways, the most common of which is Git, which is
used to manage various releases and staged deployment.