Steel production companies must utilize new technologies and innovations to stay ahead of a highly competitive market. Recently, there has been a focus on Industry 4.0, which involves the digitalization of production to integrate with newer technologies such as cloud solutions and the Internet of Things (IoT). This results in a greater understanding of processes and data gathered in production, laying the foundation for potential machine learning (ML) implementations. ML models can improve process quality, reduce energy usage to produce more environmentally friendly products, and gain competitive advantages. Implementing several ML models in production can be difficult, as it involves dealing with different datasets and algorithms, moving models into production, and post-deployment maintenance. If these tasks are kept manually, the workload quickly becomes too large to handle effectively. This is why machine learning operations (MLOps) has recently been a popular topic. Automating parts of the ML workflow enables these systems to scale effectively as the number of models increases. This thesis aims to investigate how implementing MLOps practices can help an organization increase its use of ML systems. To do this, an MLOps framework is implemented using Microsoft Azure services together with a dataset from the stakeholder Uddeholm AB. The resulting workflow consists of automated pipelines for data pre-processing, training, and deployment of an ML model, contributing to establishing a scalable ML framework. Automating the majority of the workflow greatly eases the workload for managing the lifecycle of ML models.