Update content/v1/ai-sentiment-analysis-api-tutorial.md

Co-authored-by: itowlson <[email protected]>
Signed-off-by: Vaughn Dice <[email protected]>

Author: vdice

content/v1/ai-sentiment-analysis-api-tutorial.md

@@ -887,7 +887,7 @@ route = "/internal/kv-explorer/..."
 
 ### Building and Deploying Your Spin Application
 
-**Note:** Running inferencing on localhost (your CPU) is not as optimal as deploying to Fermyon Cloud's Serverless AI (where inferencing is performed by high-powered GPUs). You can skip this `spin build --up` step and move straight to `spin cloud deploy` if you:
+**Note:** Running inferencing on localhost (your CPU) is not as optimal as deploying to a dedicated Serverless AI platform (where inferencing is performed by high-powered GPUs). You can skip this `spin build --up` step and move straight to deployment if you:
 
 - a) are using one of the 3 supported models above,
 - b) have configured your `spin.toml` file to explicitly configure the model (as shown above)

content/v2/ai-sentiment-analysis-api-tutorial.md

@@ -1048,7 +1048,7 @@ key_value_stores = ["default"]
 
 ### Building and Deploying Your Spin Application
 
-**Note:** Running inferencing on localhost (your CPU) is not as optimal as deploying to Fermyon Cloud's Serverless AI (where inferencing is performed by high-powered GPUs). You can skip this `spin build --up` step and move straight to `spin cloud deploy` if you:
+**Note:** Running inferencing on localhost (your CPU) is not as optimal as deploying to a dedicated Serverless AI platform (where inferencing is performed by high-powered GPUs). You can skip this `spin build --up` step and move straight to deployment if you:
 
 - a) are using one of the 3 supported models above,
 - b) have configured your `spin.toml` file to explicitly configure the model (as shown above)

content/v3/ai-sentiment-analysis-api-tutorial.md

@@ -1069,7 +1069,7 @@ files = [{ source = "assets", destination = "/" }]
 
 ### Building and Deploying Your Spin Application
 
-**Note:** Running inferencing on localhost (your CPU) is not as optimal as deploying to Fermyon Cloud's Serverless AI (where inferencing is performed by high-powered GPUs). You can skip this `spin build --up` step and move straight to `spin cloud deploy` if you:
+**Note:** Running inferencing on localhost (your CPU) is not as optimal as deploying to a dedicated Serverless AI platform (where inferencing is performed by high-powered GPUs). You can skip this `spin build --up` step and move straight to deployment if you:
 
 - a) are using one of the 3 supported models above,
 - b) have configured your `spin.toml` file to explicitly configure the model (as shown above)