Experiments with MLflow

Machine Learning requires experimenting with a wide range of datasets, data preparation steps, and algorithms to build a model that maximizes a target metric. Once you have built a model, you also need to deploy it to a production system, monitor its performance, and continuously retrain it on new data and compare it with alternative models.

Cloudera AI lets you train, reuse, and deploy models with any library, and package them into reproducible artifacts that other data scientists can use.

Cloudera AI packages the ML models in a reusable, reproducible form so you can share them with other data scientists or transfer them to production.

Cloudera AI is compatible with the MLflow™ tracking API and uses the MLflow client library as the default method to log experiments.

The functionality described in this document is for the new version of the Experiments feature, which replaces an older version that could not be used from within Sessions. In Projects that have existing Experiments created using the previous feature, you can continue to view these existing Experiments. New projects use the new Experiments feature.

MLflow has been integrated into Cloudera AI through the development of a native plugin. This plugin acts as an interface between Cloudera API V2 and the MLflow SDK.