ml-ops.org

In this section, we provide a high-level overview of a typical workflow for machine learning-based software development. Generally, the goal of a machine learning project is to build a statistical model by using collected data and applying machine learning algorithms to them. Therefore, every ML-based software includes three main artifacts: Data, ML Model, and Code. The Figure below shows the core steps involved in a typical ML workflow. The initial step in any data science workflow is to acquire and prepare the data to be analyzed.