Merge pull request #57219 from heatherbshapiro/patch-11

updated code for new sdk version

Author: PRMerger16

articles/machine-learning/service/how-to-track-experiments.md

@@ -17,7 +17,7 @@ ms.date: 09/24/2018
 In the Azure Machine Learning service, you can track your experiments and monitor metrics to enhance the model creation process. In this article, you'll learn about the different ways to add logging to your training script, how to submit the experiment with **start_logging** and **ScriptRunConfig**, how to check the progress of a running job, and how to view the results of a run. 
 
 >[!NOTE]
-> Code in this article was tested with Azure Machine Learning SDK version 0.168 
+> Code in this article was tested with Azure Machine Learning SDK version 0.1.74 
 
 ## List of training metrics 
 
@@ -63,7 +63,6 @@ Before adding logging and submitting an experiment, you must set up the workspac
 
   # make up an arbitrary name
   experiment_name = 'train-in-notebook'
-  exp = Experiment(workspace_object = ws, name = experiment_name)
   ```
 
 ## Option 1: Use start_logging
@@ -99,7 +98,8 @@ The following example trains a simple sklearn Ridge model locally in a local Jup
 2. Add experiment tracking using the Azure Machine Learning service SDK, and upload a persisted model into the experiment run record. The following code adds tags, logs, and uploads a model file to the experiment run.
 
   ```python
-  run = Run.start_logging(experiment = exp)
+  experiment = Experiment(workspace = ws, name = experiment_name)
+  run = experiment.start_logging()
   run.tag("Description","My first run!")
   run.log('alpha', 0.03)
   reg = Ridge(alpha = 0.03)
@@ -205,8 +205,8 @@ This example expands on the basic sklearn Ridge model from above. It does a simp
   ```python
   from azureml.core import ScriptRunConfig
 
-  src = ScriptRunConfig(source_directory = script_folder, script = 'train.py', run_config = run_config_user_managed)
-  run = exp.submit(src)
+  src = ScriptRunConfig(source_directory = './', script = 'train.py', run_config = run_config_user_managed)
+  run = experiment.submit(src)
   ```
 
 ## View run details