MLflow 0.8.0 released with improved UI experience and better support for deployment

MLflow 0.8.0 released with improved UI experience and better support for deployment

Last week, the team at Databricks released MLflow 0.8.0. MLflow, an open source platform used for managing end-to-end machine learning lifecycle. It is used for tracking experiments and managing and deploying models from a variety of ML libraries. It is also responsible for packaging ML code in a reusable and reproducible form in order to share the same with other data scientists.

MLflow 0.8.0 features

In MLflow 0.8.0, the SageMaker and pyfunc server support the ‘split’ JSON format, which helps the client to specify the order of columns.

With MLflow 0.8.0, the server can now pass the gunicorn option. This is because as gunicorn uses threads instead of processes and saves memory space.

This version also brings in TensorFlow 1.12 support. With this version, there’s no need of loading Keras module at predict time.

Major change

In MLflow 0.8.0 version, [CLI] mlflow sklearn server has been removed in favor of mlflow pyfunc serve, as it takes the same arguments but works against any pyfunc model.

Major improvements in MLflow 0.8.0

This version includes various new features including improved UI experience and support for deploying models directly to the Azure Machine Learning Service Workspace.

Improved MLflow UI Experience

In this version, the metrics and parameters are by default grouped together in a single tabular column in order to avoid an explosion of columns.

The users can customize their view by sorting the parameters and metrics. They can click on each parameter or metric in order to view them in a separate column. This makes the user experience better.

The runs which are nested inside other runs can now be grouped by their parent-run. They can also be expanded or collapsed altogether.

By calling mlflow.start_run or mlflow.run, a run can be nested.

Though MLflow gives each run a UUID by default, one can also now assign a name to a run and also can edit the names. It makes the process easy as it is easier to remember the name than a number.

There’s no need to reconfigure the view each time one uses it, as the MLflow UI remembers the filters, sorting and column setup done in browser local storage.

Support for Deployment of models to Azure ML Service

In this version, the Microsoft Azure Machine Learning deployment tool has been modified for deploying MLflow models packaged as Docker containers.

One can use the mlflow.azureml module to package a python_function model into an Azure ML container image. Further, this image can be deployed to the Azure Kubernetes Service (AKS) and the Azure Container Instances (ACI) platforms.