  The accelerated adoption of AI-based software demands precise development
guidelines to guarantee reliability, scalability, and ethical compliance. MLOps
(Machine Learning and Operations) guidelines have emerged as the principal
reference in this field, paving the way for the development of high-level
automated tools and applications. Despite the introduction of MLOps guidelines,
there is still a degree of skepticism surrounding their implementation, with a
gradual adoption rate across many companies. In certain instances, a lack of
awareness about MLOps has resulted in organizations adopting similar approaches
unintentionally, frequently without a comprehensive understanding of the
associated best practices and principles. The objective of this study is to
gain insight into the actual adoption of MLOps (or comparable) guidelines in
different business contexts. To this end, we surveyed practitioners
representing a range of business environments to understand how MLOps is
adopted and perceived in their companies. The results of this survey also shed
light on other pertinent aspects related to the advantages and challenges of
these guidelines, the learning curve associated with them, and the future
trends that can be derived from this information. This study aims to provide
deeper insight into MLOps and its impact on the next phase of innovation in
machine learning. By doing so, we aim to lay the foundation for more efficient,
reliable, and creative AI applications in the future.
