  Machine learning technologies have demonstrated immense capabilities in
various domains. They play a key role in the success of modern businesses.
However, adoption of machine learning technologies has a lot of untouched
potential. Cost of developing custom machine learning solutions that solve
unique business problems is a major inhibitor to far-reaching adoption of
machine learning technologies. We recognize that the monolithic nature
prevalent in today's machine learning applications stands in the way of
efficient and cost effective customized machine learning solution development.
In this work we explore the benefits of modular machine learning solutions and
discuss how modular machine learning solutions can overcome some of the major
solution engineering limitations of monolithic machine learning solutions. We
analyze the trade-offs between modular and monolithic machine learning
solutions through three deep learning problems; one text based and the two
image based. Our experimental results show that modular machine learning
solutions have a promising potential to reap the solution engineering
advantages of modularity while gaining performance and data advantages in a way
the monolithic machine learning solutions do not permit.

Machine Learning;Software Engineering