acrolinx fixes

Author: mrbullwinkle

articles/ai-services/openai/how-to/fine-tuning-deploy.md

@@ -1,7 +1,7 @@
 ---
-title: 'Deploy a finetuned model'
+title: 'Deploy a fine-tuned model'
 titleSuffix: Azure OpenAI
-description: Learn how to deploy your finetuned model with Azure OpenAI Service by using Python, the REST APIs, or Azure AI Foundry portal.
+description: Learn how to deploy your fine-tuned model with Azure OpenAI Service by using Python, the REST APIs, or Azure AI Foundry portal.
 #services: cognitive-services
 manager: nitinme
 ms.service: azure-ai-openai
@@ -12,15 +12,15 @@ author: mrbullwinkle
 ms.author: mbullwin
 ---
 
-# Deploy a finetuned model for inferecing
+# Deploy a fine-tuned model for inferencing
 
 Once your model is fine-tuned, you can deploy the model and can use it in your own application.
 
 When you deploy the model, you make the model available for inferencing, and that incurs an hourly hosting charge. Fine-tuned models, however, can be stored in Azure AI Foundry at no cost until you're ready to use them.
 
 Azure OpenAI provides choices of deployment types for fine-tuned models on the hosting structure that fits different business and usage patterns: **Standard**, **Global Standard** (preview) and **Provisioned Managed** (preview). Learn more about [deployment types for fine-tuned models](#deployment-types) and the [concepts of all deployment types](./deployment-types.md).
 
-## Deploy your finetuned model
+## Deploy your fine-tuned model
 
 ## [Portal](#tab/portal)
 
@@ -398,7 +398,7 @@ Provisioned Managed fine-tuning deployments currently do not support vision and
 
 To create a new deployment, make an HTTP PUT call via the [Deployments - Create or Update REST API](/rest/api/aiservices/accountmanagement/deployments/create-or-update?view=rest-aiservices-accountmanagement-2024-10-01&tabs=HTTP&preserve-view=true). The approach is similar to performing [cross region deployment](#cross-region-deployment) with the following exceptions:
 
-- You must provide a sku name of `ProvisionedStandard`.
+- You must provide a `sku` name of `ProvisionedStandard`.
 - The capacity must be declared in PTUs.
 - The `api-version` must be `2024-10-01` or newer.
 - The HTTP method should be `PUT`.
@@ -449,7 +449,7 @@ curl -X PUT "https://management.azure.com/subscriptions/<SUBSCRIPTION>/resourceG
 
 To delete a deployment, use the [Deployments - Delete REST API](/rest/api/aiservices/accountmanagement/deployments/delete?view=rest-aiservices-accountmanagement-2024-10-01&tabs=HTTP&preserve-view=true) and send an HTTP DELETE to the deployment resource. Like with creating deployments, you must include the following parameters:
 
-- Azure subscription id
+- Azure subscription ID
 - Azure resource group name
 - Azure OpenAI resource name
 - Name of the deployment to delete

articles/ai-services/openai/how-to/fine-tuning-troubleshoot.md

@@ -21,17 +21,17 @@ In order to successfully access fine-tuning, you need **Cognitive Services OpenA
 
 ## Why did my upload fail?
 
-If your file upload fails in Azure AI Foundry portal, you can view the error message under **Data files** in Azure AI Foundry portal. Hover your mouse over where it says “error” (under the status column) and an explanation of the failure will be displayed.
+If your file upload fails in Azure AI Foundry portal, you can view the error message under **Data files** in Azure AI Foundry portal. Hover your mouse over where it says **error** (under the status column) and an explanation of the failure will be displayed.
 
 :::image type="content" source="../media/fine-tuning/error.png" alt-text="Screenshot of fine-tuning error message." lightbox="../media/fine-tuning/error.png":::
 
-## My fine-tuned model does not seem to have improved
+## My fine-tuned model doesn't seem to have improved
 
-- **Missing system message:** You need to provide a system message when you fine tune; you will want to provide that same system message when you use the fine-tuned model. If you provide a different system message, you may see different results than what you fine-tuned for.
+- **Missing system message:** You need to provide a system message when you fine tune; you'll want to provide that same system message when you use the fine-tuned model. If you provide a different system message, you may see different results than what you fine-tuned for.
 
 - **Not enough data:** while 10 is the minimum for the pipeline to run, you need hundreds to thousands of data points to teach the model a new skill. Too few data points risks overfitting and poor generalization. Your fine-tuned model may perform well on the training data, but poorly on other data because it has memorized the training examples instead of learning patterns. For best results, plan to prepare a data set with hundreds or thousands of data points.
 
-- **Bad data:** A poorly curated or unrepresentative dataset will produce a low-quality model. Your model may learn inaccurate or biased patterns from your dataset. For example, if you are training a chatbot for customer service, but only provide training data for one scenario (e.g. item returns) it will not know how to respond to other scenarios. Or, if your training data is bad (contains incorrect responses), your model will learn to provide incorrect results.
+- **Bad data:** A poorly curated or unrepresentative dataset will produce a low-quality model. Your model may learn inaccurate or biased patterns from your dataset. For example, if you're training a chatbot for customer service, but only provide training data for one scenario (e.g. item returns) it will not know how to respond to other scenarios. Or, if your training data is bad (contains incorrect responses), your model will learn to provide incorrect results.
 
 ## Fine-tuning with vision
 
@@ -43,13 +43,13 @@ Your images can get skipped for the following reasons:
 - contains people
 - contains faces
 
-Remove the image. For now, we cannot fine-tune models with images containing these entities.
+Remove the image. For now, we can't fine-tune models with images containing these entities.
 
 **Common issues**
 
 |Issue| Reason/Solution|
 |:----|:-----|
-|**Images skipped**| Images can get skipped for the following reasons: contains CAPTCHAs, people, or faces.<br><br> Remove the image. For now, we cannot fine-tune models with images containing these entities.|
+|**Images skipped**| Images can get skipped for the following reasons: contains CAPTCHAs, people, or faces.<br><br> Remove the image. For now, we can't fine-tune models with images containing these entities.|
 |**Inaccessible URL**| Check that the image URL is publicly accessible.|
 |**Image too large**| Check that your images fall within our dataset size limits.|
 |**Invalid image format**| Check that your images fall within our dataset format.|