Make your data science workflow efficient and reproducible with MLflow

This blog post was co-authored by Parashar Shah, Senior Program Manager, Applied AI Developer COGS. 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. With all the bookkeeping involved, it is easy to miss a test case, or waste time by repeating an experiment unnecessarily. After they finalize the model that they want to use for predictions, they have to do multiple things in order to create a deployment environment and then create a webservice (http endpoint) from their model.