In this post, we will describe a couple of major MLflow v0.8.0 features: An improved MLflow UI experience for tracking and categorizing experiments Support for deploying models in Docker containers to Azure Machine Learning Service Improved MLflow UI Experience Compact Display for Metrics and Parameters: To avoid clutter and an explosion of columns for each metric or parameter, now we group them together in a single tabular column by default..That way, each runs’ parameters and metrics are listed nearby..Users can still click each parameter or metric to display it in a separate column or sort by it and customize their view this way..Nesting Runs: For nested MLflow runs, which are common in hyperparameter search or multi-step workflows, the UI will display a collapsible tree underneath each parent run..This makes it much easier to organize and visualize multi-step workflows..Labeling Runs: While MLflow gives each run a UUID by default, you can also now assign each run a name through the API..These names can also be edited in the UI..UI Persistence: The MLflow UI now remembers your filters, sorting and column setup in browser local storage so you no longer need to reconfigure the view each time..Let’s look at one of these features in more detail — visualizing nested runs..First, we can use the following code to create nested default runs: # # nested default runs # with mlflow.start_run(nested=True): mlflow.log_param(“mse”, 0.10) mlflow.log_param(“lr”, 0.05) mlflow.log_param(“batch_size”, 512) with mlflow.start_run(nested=True): mlflow.log_param(“max_runs”, 32) mlflow.log_param(“epochs”, 20) mlflow.log_metric(“acc”, 98) mlflow.log_metric(“rmse”, 98) mlflow.end_run() The MLflow UI will now display these runs in a tree and let you expand them: In practice, of course, you usually won’t create nested runs in a single Python program as above..Instead, they will come up when you run multi-step workflows or hyperparameter search..MLflow includes examples of both workflows and hyperparameter search.. More details