| dc.description.abstract | The growing involvement of machine learning in businesses brings both advantages and
challenges. One of the biggest challenges is what it take to actually bring a model to production.
MLOps, inspired by the already famous DevOps concept widely adopted in Software Engineering,
appeared as the solution to the problem. Since then, many projects have sprung up and compete
each other to provide a proof of concept for MLOps. Most are young and immature, but some
achieved huge success and skyrocketed in popularity. This thesis will point out some of the
serious issues in one of the best and most popular MLOps platform at the time and then make
some contributions to it in order to overcome those issues and then package them as my own
distribution of the tool. | en_US |
