fix broken links

Author: sdgilley

articles/machine-learning/how-to-create-attach-compute-studio.md

@@ -87,7 +87,7 @@ To detach your compute use the following steps:
 
 ## Next steps
 
-* Use the compute resource to [submit a training run](how-to-set-up-training-targets.md).
+* Use the compute resource to [submit a training run](how-to-train-sdk.md).
 * Learn how to [efficiently tune hyperparameters](how-to-tune-hyperparameters.md) to build better models.
 * Once you have a trained model, learn [how and where to deploy models](how-to-deploy-managed-online-endpoints.md).
 * [Use Azure Machine Learning with Azure Virtual Networks](./how-to-network-security-overview.md)

articles/machine-learning/v1/concept-automated-ml-v1.md

@@ -188,7 +188,7 @@ Using **Azure Machine Learning**, you can design and run your automated ML train
 
 1. **Specify the source and format of the labeled training data**: Numpy arrays or Pandas dataframe
 
-1. **Configure the compute target for model training**, such as your [local computer, Azure Machine Learning Computes, remote VMs, or Azure Databricks with SDK v1](../how-to-set-up-training-targets.md).
+1. **Configure the compute target for model training**, such as your [local computer, Azure Machine Learning Computes, remote VMs, or Azure Databricks with SDK v1](how-to-set-up-training-targets.md).
 
 1. **Configure the automated machine learning parameters** that determine how many iterations over different models, hyperparameter settings, advanced preprocessing/featurization, and what metrics to look at when determining the best model.  
 1. **Submit the training job.**

articles/machine-learning/v1/how-to-set-up-training-targets.md

@@ -110,7 +110,7 @@ myenv.python.user_managed_dependencies = True
 
 ## Create the script job configuration
 
-Now that you have a compute target (`my_compute_target`, see [Prerequisites](#prerequisites) and environment (`myenv`, see [Create an environment](#environment)), create a script job configuration that runs your training script (`train.py`) located in your `project_folder` directory:
+Now that you have a compute target (`my_compute_target`, see [Prerequisites](#prerequisites) and environment (`myenv`, see [Create an environment](#create-an-environment)), create a script job configuration that runs your training script (`train.py`) located in your `project_folder` directory:
 
 ```python
 from azureml.core import ScriptRunConfig
@@ -215,7 +215,7 @@ See these notebooks for examples of configuring jobs for various training scenar
 * **Job or experiment deletion**:  Experiments can be archived by using the [Experiment.archive](/python/api/azureml-core/azureml.core.experiment%28class%29#archive--) 
 method, or from the Experiment tab view in Azure Machine Learning studio client via the "Archive experiment" button. This action hides the experiment from list queries and views, but does not delete it.
 
-    Permanent deletion of individual experiments or jobs is not currently supported. For more information on deleting Workspace assets, see [Export or delete your Machine Learning service workspace data](v1/how-to-export-delete-data.md).
+    Permanent deletion of individual experiments or jobs is not currently supported. For more information on deleting Workspace assets, see [Export or delete your Machine Learning service workspace data](how-to-export-delete-data.md).
 
 * **Metric Document is too large**: Azure Machine Learning has internal limits on the size of metric objects that can be logged at once from a training job. If you encounter a "Metric Document is too large" error when logging a list-valued metric, try splitting the list into smaller chunks, for example:
 
@@ -226,12 +226,12 @@ method, or from the Experiment tab view in Azure Machine Learning studio client
 
     Internally, Azure ML concatenates the blocks with the same metric name into a contiguous list.
 
-* **Compute target takes a long time to start**: The Docker images for compute targets are loaded from Azure Container Registry (ACR). By default, Azure Machine Learning creates an ACR that uses the *basic* service tier. Changing the ACR for your workspace to standard or premium tier may reduce the time it takes to build and load images. For more information, see [Azure Container Registry service tiers](../container-registry/container-registry-skus.md).
+* **Compute target takes a long time to start**: The Docker images for compute targets are loaded from Azure Container Registry (ACR). By default, Azure Machine Learning creates an ACR that uses the *basic* service tier. Changing the ACR for your workspace to standard or premium tier may reduce the time it takes to build and load images. For more information, see [Azure Container Registry service tiers](container-registry/container-registry-skus.md).
 
 ## Next steps
 
 * [Tutorial: Train and deploy a model](tutorial-1st-experiment-sdk-train.md) uses a managed compute target to  train a model.
-* See how to train models with specific ML frameworks, such as [Scikit-learn](../-to-train-scikit-learn.md), [TensorFlow](../how-to-train-tensorflow.md), and [PyTorch](../how-to-train-pytorch.md).
+* See how to train models with specific ML frameworks, such as [Scikit-learn](../how-to-train-scikit-learn.md), [TensorFlow](../how-to-train-tensorflow.md), and [PyTorch](../how-to-train-pytorch.md).
 * Learn how to [efficiently tune hyperparameters](../how-to-tune-hyperparameters.md) to build better models.
 * Once you have a trained model, learn [how and where to deploy models](../how-to-deploy-managed-online-endpoints.md).
 * View the [ScriptRunConfig class](/python/api/azureml-core/azureml.core.scriptrunconfig) SDK reference.