  Applying DevOps practices to machine learning system is termed as MLOps and
machine learning systems evolve on new data unlike traditional systems on
requirements. The objective of MLOps is to establish a connection between
different open-source tools to construct a pipeline that can automatically
perform steps to construct a dataset, train the machine learning model and
deploy the model to the production as well as store different versions of model
and dataset. Benefits of MLOps is to make sure the fast delivery of the new
trained models to the production to have accurate results. Furthermore, MLOps
practice impacts the overall quality of the software products and is completely
dependent on open-source tools and selection of relevant open-source tools is
considered as challenged while a generalized method to select an appropriate
open-source tools is desirable. In this paper, we present a framework for
recommendation system that processes the contextual information (e.g., nature
of data, type of the data) of the machine learning project and recommends a
relevant toolchain (tech-stack) for the operationalization of machine learning
systems. To check the applicability of the proposed framework, four different
approaches i.e., rule-based, random forest, decision trees and k-nearest
neighbors were investigated where precision, recall and f-score is measured,
the random forest out classed other approaches with highest f-score value of
0.66.
