MLOps: train your model using Azureml

In your journey to learn machine learning and data science, there's a high chance that you've came across "MLOps" term mentioned in some of those online tutorials and articles, but you probably thought it's just another fancy word invented to add an extra layer of complexity to the DS world. MLOps stands for Machine Learning Operations, it refers to the set of practices that improves the quality of machine learning an AI projects, and makes their lifecycle management much easier and reproducible for all stakeholders. Productionizing ML projects is difficult. The machine learning lifecycle consists of many complex components such as data ingestion, preprocessing, model training, hyperparameters tuning, model deployment, and performance monitoring... etc. It also requires collaboration and hand-offs across teams, from Data Engineering to Data Science to ML Engineering.