Description
Day 1, R1 15:55–16:10
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.
Slides: https://speakerdeck.com/sucitw/track-machine-learning-applications-by-mlflow-tracking
Speaker: 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.