The recommended articles the author has read this week.
This letter is posted every Monday.
This week, the articles are about a Machine learning project environment, e. g. MLflow, and Jupyter Notebook.
In the environment of a machine learning project, it is very important to prepare not only a data analysis environment such as Jupyter Notebook but also an MLflow environment for managing experiment records. Managing experiment records helps to improve reproducibility and project promotion efficiency.
Containerize your whole Data Science Environment (or anything you want) with Docker-Compose
To build the environment of a machine learning project, docker-compose is a powerful tool. By docker-compose, we can build data-analysis and experiment-management experiments separately. This article tells us how to build such a style environment.
Manage your machine learning life cycle with MLflow in Python
This article shows one example of how to use the MLflow tracking server, which is a tool for managing experiment records. There are several styles to build the MLflow tracking server. This article suggests one of the helpful styles.