You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Copy file name to clipboardExpand all lines: tutorials/ai-core-deploy/ai-core-deploy.md
+7-7Lines changed: 7 additions & 7 deletions
Display the source diff
Display the rich diff
Original file line number
Diff line number
Diff line change
@@ -17,9 +17,9 @@ If you are an SAP Developer or SAP employee, please refer to the following links
17
17
[How to create a BTP Account (internal)](https://me.sap.com/notes/3493139)
18
18
[SAP AI Core](https://help.sap.com/docs/sap-ai-core?version=INTERNAL&locale=en-US&state=PRODUCTION)
19
19
If you are an external developer or a customer or a partner kindly refer to this [tutorial](https://developers.sap.com/tutorials/btp-cockpit-entitlements.html)
20
-
- You have connected code to the AI workflows of SAP AI Core using [this tutorial](ai-core-code).
21
-
- You have trained a model using SAP AI Core, such as the house price predictor model in [this tutorial](ai-core-data), or your own model trained in your local system. If you trained your own local model, follow [this tutorial](ai-core-tensorflow-byod) to use it with SAP AI Core.
22
-
- You know how to locate artifacts. This is explained in [this tutorial](ai-core-data).
20
+
- You have connected code to the AI workflows of SAP AI Core using [this tutorial](ai-core-code).
21
+
- You have trained a model using SAP AI Core, such as the house price predictor model in [this tutorial](ai-core-data), or your own model trained in your local system. If you trained your own local model, follow [this tutorial](ai-core-tensorflow-byod) to use it with SAP AI Core.
22
+
- You know how to locate artifacts. This is explained in [this tutorial](ai-core-data).
23
23
24
24
## You will learn
25
25
- How to create deployment server an for AI model
@@ -32,7 +32,7 @@ You will create a deployment server for AI models to use in online inferencing.
32
32
The deployment server demonstrated in this tutorial can only be used in the backend of your AI project. For security reasons, in your real set up you will not be able to directly make prediction calls from your front end application to the deployment server. Doing so will lead to an inevitable Cross-origin Resource Sharing (CORS) error. As a temporary resolution, please deploy another application between your front end application and this deployment server. This middle application should use the SAP AI Core SDK (python package) to make calls to the deployment server.
33
33
34
34
35
-
Please find downloadable sample notebooks for the tutorials : . Note that these tutorials are for demonstration purposes only and should not be used in production environments. To execute them properly, you'll need to set up your own S3 bucket or provision services from BTP, including an AI Core with a standard plan for narrow AI and an extended plan for GenAI HUB. Ensure you input the service keys of these services into the relevant cells of the notebook.
35
+
Please find downloadable sample notebooks for the tutorials : . Note that these tutorials are for demonstration purposes only and should not be used in production environments. To execute them properly, you'll need to set up your own S3 bucket or provision services from BTP, including an AI Core with a standard plan for narrow AI and an extended plan for Generative AI Hub. Ensure you input the service keys of these services into the relevant cells of the notebook.
36
36
[Link to notebook](https://github.com/SAP-samples/ai-core-samples/blob/main/02_ai_core/tutorials/01_create_your_first_machine_learning_project_using_sap_ai_core/01_05_make_predictions_for_house_prices_with_sap_ai_core/make-prediction.ipynb)
37
37
38
38
---
@@ -339,7 +339,7 @@ Paste and edit the snippet below. Your should use the configuration ID generated
339
339
340
340
```PYTHON
341
341
response = ai_core_client.deployment.create(
342
-
configuration_id="YOUR_CONFIGURATIO_ID",
342
+
configuration_id="YOUR_CONFIGURATION_ID",
343
343
resource_group='default'
344
344
)
345
345
@@ -559,10 +559,10 @@ print(response.__dict__)
559
559
### Check Running Resources (optional)
560
560
561
561
562
-
You can check the Current running Pods Using in AI Lauchpad Choosing the Deployment and clicking on Scaling tab
562
+
You can check the current running Pods Using in AI Launchpad, choosing the Deployment, and clicking on Scaling tab
563
563
564
564
<!-- border -->
565
565
566
-
Similary if you want to check for resource plan just visit the resources tab
566
+
Similarly if you want to check for resource plan just visit the resources tab
0 commit comments