diff --git a/.docs/python_scoring.md b/.docs/python_scoring.md
index 6c29cde..02de1fe 100644
--- a/.docs/python_scoring.md
+++ b/.docs/python_scoring.md
@@ -1,7 +1,7 @@
 # Azure services for deploying Python ML
 
 <p align="center">
-  <img width="800" src="../images/decision_python_scoring.png">
+  <img width="800" src="../.images/decision_python_scoring.png">
 </p>
 
-When deploying ML models in Python there are two core questions. The first is will it be real time and whether the model is a deep learning model. For deploying deep learning models that require real time we recommend Azure Kubernetes Services (AKS) with GPUs. For a tutorial on how to do that look at [AKS w/GPU](https://github.com/Microsoft/AKSDeploymentTutorialAML). For deploying deep learning models for batch scoring we recommend using AzureML pipelines with GPUs, for a tutorial on how to do that look [AzureML Pipelines w/GPU](https://github.com/Azure/Batch-Scoring-Deep-Learning-Models-With-AML). For non deep learning models we recommend you use the same services but without GPUs. For a tutorial on deploying classical ML models for real time scoring look [AKS](https://github.com/Microsoft/MLAKSDeployAML) and for batch scoring [AzureML Pipelines](https://github.com/Microsoft/AMLBatchScoringPipeline)
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+When deploying ML models in Python there are two core questions. The first is will it be real time and whether the model is a deep learning model. For deploying deep learning models that require real time we recommend Azure Kubernetes Services (AKS) with GPUs. For a tutorial on how to do that look at [AKS w/GPU](https://github.com/Microsoft/AKSDeploymentTutorialAML). For deploying deep learning models for batch scoring we recommend using AzureML pipelines with GPUs, for a tutorial on how to do that look [AzureML Pipelines w/GPU](https://github.com/Azure/Batch-Scoring-Deep-Learning-Models-With-AML). For non deep learning models we recommend you use the same services but without GPUs. For a tutorial on deploying classical ML models for real time scoring look [AKS](https://github.com/Microsoft/MLAKSDeployAML) and for batch scoring [AzureML Pipelines](https://github.com/Microsoft/AMLBatchScoringPipeline)
diff --git a/.docs/python_training.md b/.docs/python_training.md
index cc934f7..80a5af4 100644
--- a/.docs/python_training.md
+++ b/.docs/python_training.md
@@ -1,10 +1,10 @@
 # Azure services for training Python ML models
 
 <p align="center">
-  <img width="800" src="../images/python_training_diag.png">
+  <img width="800" src="../.images/python_training_diag.png">
 </p>
 
 There are many options for training ML models in Python on Azure. The most straight forward way is to train your model on a [DSVM](https://azure.microsoft.com/en-us/services/virtual-machines/data-science-virtual-machines/). You can either do this in local model straight on the VM or through attaching it in AzureML as a compute target. If you want to have AzureML manage the compute for you and scale it up and down based on whether jobs are waiting in the queue then you should AzureML Compute. 
 
 Now if you are going to run multiple jobs for hyperparameter tuning or other purposes then we would recommend using [Hyperdrive](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-tune-hyperparameters), [Azure automated ML](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-automated-ml) or AzureML Compute dependent on your requirements.
-For a tutorial on how to use Hyperdrive go [here](https://github.com/Microsoft/MLHyperparameterTuning).
\ No newline at end of file
+For a tutorial on how to use Hyperdrive go [here](https://github.com/Microsoft/MLHyperparameterTuning).