DevConf.US 2019

Machine learning is more complex than traditional software development. We all know that fetching the right parameters for tuning a machine learning model is quintessential in finding the optimal model.

Have multiple machine learning models with different parameters running, but do not have a platform to track each of these models? MLFlow is the new open source platform which allows data scientists, product engineers, developers and large organizations to manage the ML lifecycle, including experimentation, reproducibility and deployment.

Attend this talk to learn how to:
1. Use MLFlow for experiment tracking
2. Perform hyperparameter model tuning with MLFlow
3. Run, compare and visualize the metrics/parameters of multiple runs of your machine learning model with MLFlow