Skip to main content

Roope Astala

Senior Program Manager

Latest posts

Showing 1 – 1 of 1 posts found

Make your data science workflow efficient and reproducible with MLflow 

When data scientists work on building a machine learning model, their experimentation often produces lots of metadata: metrics of models you tested, actual model files, as well as artifacts such as plots or log files. They often try different models and parameters, for example random forests of varying depth, linear models with different regularization rates, or deep learning models with different architectures trained using different learning rates.