Browsing by Author "Anttila, Kamilla"

Most machine learning projects consist of four distinct phases: data preparation, model training, model validation, and inference serving. Even though all of these phases are vital components of a successful machine learning project, the focus of most machine learning work is solely on the training of models. The other phases often need to be implemented with ad-hoc solutions, which can easily lead to technical debt. Technical debt is a metaphor for describing the quality of a software project. It describes the state of a project by comparing it to a financial loan. During software development, a loan can be taken to add value to the present state of the system. However, the loan comes with interest and has to be payed back. A loan can be taken, for example, by writing low quality code to meet a deadline. The loan has to be payed back by rewriting the code later, or else it will start to grow interest. The interest can be seen in the code functioning poorly or requiring substantial amounts of time to be understood. If a loan is not payed back, the interest keeps increasing, making it more and more difficult to pay the loan back later. In this thesis, we study the effect machine learning frameworks have on technical debt. We describe the machine learning project lifecycle and the various sources of technical debt associated with it. We review available machine learning frameworks and their mitigation strategies for the technical debt in machine learning projects. Our insights demonstrate how frameworks can be used to reduce the overall technical debt in machine learning projects.