updated

docs/Using_The_Azure_Price_Calculator.md

@@ -59,12 +59,12 @@ Products are available but probably not used in Azure Cloud lab.
 ### Containers
 The Containers tab displays tiles showing the options for Azure based containers.
 
-![Screenshot of Container Products](/docs/images/(9_container_products.png)
+![Screenshot of Container Products](/docs/images/9_container_products.png)
 
 ### Databases
 The Databases tab displays tiles listing your Azure database options.
 
-![Screenshot of Database Products](/docs/images/(10_db_products.png)
+![Screenshot of Database Products](/docs/images/10_db_products.png)
 
 ### Analytics
 This tab displays the available Azure based analytic tools.
@@ -74,17 +74,17 @@ This tab displays the available Azure based analytic tools.
 ### AI + machine learning
 The AI + machine learning tab displays tiles of of available products related to the title.
 
-![Screenshot of AIML Products](/docs/images/(12_AI_products.png)
+![Screenshot of AIML Products](/docs/images/12_AI_products.png)
 
 ### Internet of Things
 The IOT tab displays products related to IOT operations plus some analytics and machine learning products.
 
-![Screenshot of AIML Products](/docs/images/(13_IOT_products.png)
+![Screenshot of AIML Products](/docs/images/13_IOT_products.png)
 
 ### Integration
 This tab displays tiles of products relating to integrating with operations outside of the Azure Cloud. 
 
-![Screenshot of Integration Products](/docs/images/(14_integration_products.png)
+![Screenshot of Integration Products](/docs/images/14_integration_products.png)
 
 ### Identity
 Products are available but probably not used in Azure Cloud lab.
@@ -115,15 +115,15 @@ Products aavailable but probably not used in Azure Cloud lab.
 The Examples tab is exactly what it describes. It shows you six examples of figuring the total cost of various products that you would need for your projects. 
 If we choose Real-time analysis we are presented with a project drawing with a listing of the products that will be used. 
 
-![Screenshot of RealTimeAnaltyics](/docs/images/(15_RT_Analytics.png)
+![Screenshot of RealTimeAnaltyics](/docs/images/15_RT_Analytics.png)
 
 If you click the Add to Estimate button you will then see a breakdown of each item's cost. Please note the number of instances and the default number of hours used to generate the estimate.   
 
-![Screenshot of RealTimeAnaltyics](/docs/images/(16_get_estimate.png)
+![Screenshot of RealTimeAnaltyics](/docs/images/16_get_estimate.png)
 
 Now, if we do the same steps with the example above as we did under calculating costs by removing items and reducing hours. We can then bring the costs down to something that is under the Cloud Lab monthly budget. 
 
-![Screenshot of RealTimeAnaltyics](/docs/images/(17_revise_estimate.png)
+![Screenshot of RealTimeAnaltyics](/docs/images/17_revise_estimate.png)
 
 ## Saved Estimates Tab
 Just what it implies. You will find any previously saved estimates under this tab.

tutorials/README.md

@@ -71,7 +71,7 @@ Single-cell RNA sequencing (scRNA-seq) is a technique that enables the analysis
 
 ## **Long Read Sequence Analysis** <a name="long"></a>
 Long read DNA sequence analysis involves analyzing sequencing reads typically longer than 10 thousand base pairs (bp) in length, compared with short read sequencing where reads are about 150 bp in length.
-Oxford Nanopore has a pretty complete offering of notebook tutorials for handling long read data to do a variety of things including variant calling, RNAseq, Sars-Cov-2 analysis and much more. Access the notebooks [here](https://labs.epi2me.io/nbindex/) and on [GitHub](https://github.com/epi2me-labs/tutorials/tree/master/tutorials). These notebooks expect you are running locally and accessing the epi2me notebook server. To run them in Cloud Lab, skip the first cell that connects to the server and then the rest of the notebook should run correctly, with a few tweaks. Oxford Nanopore also offers a host of [Nextflow workflows](https://labs.epi2me.io/wfindex/) that will allow you to run a variety of long read pipelines. 
+Oxford Nanopore has a pretty complete offering of notebook tutorials for handling long read data to do a variety of things including variant calling, RNAseq, Sars-Cov-2 analysis and much more. Access the notebooks [here](https://labs.epi2me.io/nbindex/) and on [GitHub](https://github.com/epi2me-labs). These notebooks expect you are running locally and accessing the epi2me notebook server. To run them in Cloud Lab, skip the first cell that connects to the server and then the rest of the notebook should run correctly, with a few tweaks. Oxford Nanopore also offers a host of [Nextflow workflows](https://labs.epi2me.io/wfindex/) that will allow you to run a variety of long read pipelines. 
 
 ## **Open Data** <a name="open"></a>
 These publicly available datasets can save you time on data discovery and preparation by being curated and ready to use in your workflows.

tutorials/notebooks/Azure_Open_AI/README.md

@@ -70,7 +70,7 @@ Now we will look at [adding and querying over your own data](#Upload-your-own-da
 
 If you have not already (A) Navigate to the Chat Playground. Here we will walk through the various options available to you. First, you can specify a `System Message` which tells the model what context with which to respond to inquiries. To modify this, (B) select `System message`, then (B) input a [System Message](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/system-message#define-the-models-profile-capabilities-and-limitations-for-your-scenario) in the prompt box, then (D) click **Save**. 
 
-On the next tab over, you can (A) add your own data, which we dive into in the [next section](Upload-your-own-data-and-query-over-it). In the middle of the page is where you actually interact with the model (B) through the chat prompts. Always (C) clear the chat after each session. 
+On the next tab over, you can (A) add your own data, which we dive into in the [next section](#Upload-your-own-data-and-query-over-it). In the middle of the page is where you actually interact with the model (B) through the chat prompts. Always (C) clear the chat after each session. 
 
   ![add your own data](/docs/images/18_add_custom_data.png)
 

tutorials/notebooks/SpleenLiverSegmentation/README.md

@@ -1,14 +1,16 @@
 # Spleen Segmentation with Liver Example using NVIDIA Models and MONAI
 _We have put together a training example that segments the Spleen in 3D CT Images. At the end is an example of combining both the Spleen model and the Liver model._
 
+*Nvidia has changed some of the models used in this tutorial and it may crash, if you have issues, try commenting out the liver model, we are working on a patch*
+
 ## Introduction
 Two pre-trained models from NVIDIA are used in this training, a Spleen model and Liver. 
 The Spleen model is additionally retrained on the medical decathlon spleen dataset: [http://medicaldecathlon.com/](http://medicaldecathlon.com/)
 Data is not necessary to be downloaded to run the notebook. The notebook downloads the data during it's run.
 The notebook uses the Python package [MONAI](https://monai.io/), the Medical Open Network for Artificial Intelligence. 
 
-- Spleen Model - [clara_pt_spleen_ct_segmentation_V2](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/med/models/clara_pt_spleen_ct_segmentation)
-- Liver Model - [clara_pt_liver_and_tumor_ct_segmentation_V1](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/med/models/clara_pt_liver_and_tumor_ct_segmentation)
+- Spleen Model - [clara_pt_spleen_ct_segmentation_V2](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/monaitoolkit/models/monai_spleen_ct_segmentation)
+- Liver Model - [clara_pt_liver_and_tumor_ct_segmentation_V1]()
 
 ## Outcomes
 After following along with this notebook the user will be familiar with: