Enabling MLOPs in Three Simple Steps

I recently engaged in a project involving the implementation of a multiclass classification prediction system utilising financial transactional data, comprising over 10 million records and over 70 classes. Through this project, I constructed a streamlined end-to-end machine learning operations (MLOPs) infrastructure that is well-suited for this specific use case, while maintaining cost efficiency. The term MLOPs has a broad range of concepts and definitions, as offered by various vendors or solutions. Some focus on aspects such as training traceability and experimental tracking, while others prioritise feature storage or model deployment. In my understanding, MLOPs is the entire end-to-end process, from data extraction to model deployment and monitoring.