Track Machine Learning Applications by MLflow Tracking


Productization of machine learning (ML) solutions can be challenging. Therefore, the concept of operationalization on machine learning (MLOps) has emerged in the past few years for effective model lifecycle management. One of the core aspects of MLOps is "monitoring".

ML models are built by experimenting with a wide range of datasets. However, since the real data continues to change, it is necessary to monitor and to manage model usage, consumption, and results of models.

MLflow is an open-source framework designed to manage the end-to-end ML lifecycle with different components. In the talk, the basic concepts of MLflow will be introduced. Then, MLflow Tracking will be the main focus. You will know how to track experiments for recording and comparing parameters and results by MLflow Tracking.


The consumption and usage of ML models highly rely on new training or real-world data. Therefore, how to track and monitor ML applications are very challenging. That will be the main focus for this sharing. This talk will start with the concept of logging, then extend to why we should monitor ML application. Finally, the sharing will turn to address how to track and monitor ML applications via MLflow Tracking with good practices. #### Materials and sample code * #### Reference * [MLflow Offical Doc]( * [MLflow tracking](



Shuhsi Lin

A data engineer and python programmer. Currently working on various data applications in a manufacturing company.

Research interests: IoT applications, data streaming processing, data analysis and data visualization.