Merge pull request #12578 from MicrosoftDocs/learn-build-service-prod…

…bot/docutune-autopr-20240909-050631-4362571-ignore-build

[DocuTune-Remediation] - Scheduled execution to fix known issues in Azure Architecture Center articles (part 4)

docs/example-scenario/ai/real-time-anomaly-detection-conveyor-belt-content.md

@@ -1,8 +1,8 @@
 The manufacturing industry is undergoing revolutionary changes as an increasing number of firms adopt smart factory floors enabled by AI and machine learning. This article provides an overview of an architecture to enable real-time anomaly detection for conveyor belts.
- 
+
 ## Architecture
 
-:::image type="content" source="media/real-time-anomaly-detection.png" alt-text="Architecture diagram that shows a solution for real-time anomaly detection." lightbox="media/real-time-anomaly-detection.png" border="false"::: 
+:::image type="content" source="media/real-time-anomaly-detection.png" alt-text="Architecture diagram that shows a solution for real-time anomaly detection." lightbox="media/real-time-anomaly-detection.png" border="false":::
 
 *Download a [Visio file](https://arch-center.azureedge.net/realtime-anomaly-detection.vsdx) of this architecture.*
 
@@ -11,25 +11,25 @@ The manufacturing industry is undergoing revolutionary changes as an increasing
 1. Data source
 
    A sophisticated data-collection sensor is a crucial Internet of Things (IoT) component. Sensors collect analog data from the physical world and translate it into digital data assets. Sensors can measure just about any aspect of the physical world. The calibration of sensors allows them to be tailored to application-specific functions. In this dataset, sensors are calibrated to accurately measure temperature and vibrations.
-   
+
    On most factory floors, conveyor belts run on schedules. Anomaly detection of temperature and vibrations is needed when the conveyor belt is running. Time Series API is used to capture and relay conveyor belt status.
 
 1. Ingest
 
    We recommend [Azure IoT Hub](/azure/iot-fundamentals/iot-introduction) for streaming data from IoT sensors and connecting IoT devices. For ingesting data from Time Series API and data orchestration, we recommend [Azure Data Factory](/azure/data-factory/introduction).
 
 1. Store
-   
+
    Data collected from IoT sensors (temperature and vibrations) and Time Series API (conveyor belt status) are all time series. Time series data is a collection of observations obtained through repeated measurements over time. This data is collected as flat files. Each flat file is tagged with an IoT sensor ID and the date and time of collection and stored in [Azure Data Lake](https://azure.microsoft.com/solutions/data-lake).
 
 1. AI / machine learning - data preparation and training
 
    *Data preparation* is the process of gathering, combining, structuring, and organizing data so it can be used to build machine learning models, business intelligence (BI), and analytics and data visualization applications.
-   
+
    [Azure Databricks](/azure/databricks/scenarios/what-is-azure-databricks) is used to prepare the data before the data is used to build models. Azure Databricks provides an interactive workspace that enables collaboration between data engineers, data scientists, and machine learning engineers. In analytics workflow, Azure Databricks is used to read data from [Azure Data Lake](https://azure.microsoft.com/solutions/data-lake) to perform data wrangling and data exploration.
 
    *Model training* is the process of using a machine learning algorithm to learn patterns based on data and pick a suitable model for making predictions.
- 
+
    [Azure Machine learning](https://azure.microsoft.com/services/machine-learning) is used to train the model. Azure Machine Learning is a cloud service that accelerates and manages the machine learning project lifecycle. The lifecycle includes training, deploying models, and managing machine learning operations (MLOps).
 
 1. AI / machine learning - inference
@@ -38,7 +38,7 @@ The manufacturing industry is undergoing revolutionary changes as an increasing
 
    The model registry is built into [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning). It's used to store and version models in Azure. The model registry makes it easy to organize and keep track of trained models.
 
-   After a machine learning model is trained, the model needs to be deployed so that newly available data can be fed through it for inferencing. The recommended deployment target is an [Azure managed endpoint](/azure/machine-learning/concept-endpoints). 
+   After a machine learning model is trained, the model needs to be deployed so that newly available data can be fed through it for inferencing. The recommended deployment target is an [Azure managed endpoint](/azure/machine-learning/concept-endpoints).
 
 1. Analytical workload
 
@@ -87,13 +87,12 @@ The data necessary to predictively maintain motors attached to conveyor belts ar
 
 **Temperature:** Sensors attached to conveyor belts and the factory floor can record the temperature of the motor and baseline the ambient temperature. Temperature is seasonally affected because of sunlight exposure, air conditioning settings, and numerous other factors. You need to address the seasonal aspect of temperature. There are many ways to do so. One method, if we take motor temperature as an example, is to subtract the baseline ambient temperature of the factory floor from the motor temperature:
 
-*(Adjusted Temperature = Motor Temperature - Ambient Temperature)*
+*(Adjusted Temperature = Motor Temperature - Ambient Temperature)*
 
  This sample graph shows temperatures recorded from motors and the ambient baseline temperature:
 
 :::image type="content" source="media/motor-ambient-baseline-temperatures.png" alt-text="Graph that shows temperatures recorded from motors and the ambient baseline temperature." lightbox="media/motor-ambient-baseline-temperatures.png" border="false":::
 
-
 The following sample graph shows how the temperature from a conveyor belt motor is adjusted for seasonality by using the ambient temperature of the factory floor. It also shows anomalies, in red, that are detected by a model that uses the architecture suggested in this article.
 
 :::image type="content" source="media/temperatures-adjusted-anomalies.png" alt-text="Graph that shows how temperatures are adjusted for seasonality. It also shows anomalies." lightbox="media/temperatures-adjusted-anomalies.png" border="false":::
@@ -118,7 +117,7 @@ You can apply this solution to the following scenarios:
 ## Considerations
 
 These considerations implement the pillars of the Azure Well-Architected Framework, which is a set of guiding tenets that you can use to improve the quality of a workload. For more information, see the [Microsoft Azure Well-Architected Framework](/azure/well-architected/).
- 
+
 The technologies in this architecture were chosen for scalability and availability, with the aim of managing and controlling costs.
 
 Azure Industrial IoT can help you accelerate your path to modernize your connected factory. Also, Azure Digital Twins can help you to model the connected physical environments in a manufacturing setup. For more information, see these resources:
@@ -182,7 +181,7 @@ Principal authors:
 
 Other contributors:
 
-- [Mick Alberts](https://www.linkedin.com/in/mick-alberts-a24a1414) | Technical Writer 
+- [Mick Alberts](https://www.linkedin.com/in/mick-alberts-a24a1414) | Technical Writer
 - [Charitha Basani](https://www.linkedin.com/in/charitha-basani-54196031) | Senior Cloud Solution Architect, US National CSA Team
 
 *To see non-public LinkedIn profiles, sign in to LinkedIn.*

docs/example-scenario/ai/risk-stratification-surgery-content.md

@@ -11,7 +11,7 @@ AI and machine learning play a pivotal role when it comes to surgical interventi
 1. Data source
 
    Patient-centric data is sourced from [Fast Healthcare Interoperability Resources (FHIR®)](https://www.hl7.org/fhir/index.html), real-time Electronic Health Records (EHR), on-premises, and third-party data sources.
-   
+
    > [!IMPORTANT]
    > When you use patient-centric data, you need to be sure that personally identifiable data is carefully handled and is excluded from the training and test dataset.
 
@@ -20,32 +20,32 @@ AI and machine learning play a pivotal role when it comes to surgical interventi
    - Patient demographic information
    - Information about existing comorbidities and their severity
    - Information about the patient's current medication plan
-   - Patient pre-operative blood test information 
-   - Other critical health-related information  
+   - Patient pre-operative blood test information
+   - Other critical health-related information
 
 1. Data preparation
 
    *Data preparation* is the process of gathering, combining, structuring, and organizing data so that you can use it to build machine learning models, business intelligence (BI), and analytics and data visualization applications.
 
-   - [Azure Data Factory](/azure/data-factory/introduction) transforms, orchestrates, and loads data that's ready for further processing. 
-   - [Azure API for FHIR](/azure/healthcare-apis/azure-api-for-fhir/overview) enables the rapid exchange of data. 
-   - [Azure Synapse Analytics](/azure/synapse-analytics/index) processes data and triggers Azure Machine Learning experiments. 
+   - [Azure Data Factory](/azure/data-factory/introduction) transforms, orchestrates, and loads data that's ready for further processing.
+   - [Azure API for FHIR](/azure/healthcare-apis/azure-api-for-fhir/overview) enables the rapid exchange of data.
+   - [Azure Synapse Analytics](/azure/synapse-analytics/index) processes data and triggers Azure Machine Learning experiments.
    - [Azure Data Lake](https://azure.microsoft.com/solutions/data-lake) stores tabular data that describes patient-centric information in flat files.
 
 1. AI / machine learning - training
 
    *Model training* is the process of using a machine learning algorithm to learn patterns based on data and picking a model that's capable of predicting the surgery risk of previously unseen patients.
- 
-   [Azure Machine Learning](/azure/machine-learning/overview-what-is-azure-machine-learning) trains the model. Azure Machine Learning is a cloud service that accelerates and manages the machine learning project lifecycle. The lifecycle includes training models, deploying models, and managing Machine Learning Operations (MLOps). 
- 
+
+   [Azure Machine Learning](/azure/machine-learning/overview-what-is-azure-machine-learning) trains the model. Azure Machine Learning is a cloud service that accelerates and manages the machine learning project lifecycle. The lifecycle includes training models, deploying models, and managing Machine Learning Operations (MLOps).
+
    For this use case, you need to use models that can be explained. With the help of the interactive interpretability dashboard in [Responsible AI Toolbox](https://responsibleaitoolbox.ai), stakeholders can clearly understand the factors that play a key role in determining a particular risk for all patients. Responsible AI Toolbox also provides interpretation at the patient level. This interpretation helps clinicians to customize treatments for specific treatments.
 
-   Responsible AI Toolbox provides an interactive dashboard for detecting bias towards protected classes like gender and race in models. Because the training data is based on patients who have undergone the surgery, stakeholders need to understand any inherent biases in the data that the model has picked up. When the chosen model is biased towards protected classes, you can use Responsible AI Toolbox for model mitigation.
+   Responsible AI Toolbox provides an interactive dashboard for detecting bias toward protected classes like gender and race in models. Because the training data is based on patients who have undergone the surgery, stakeholders need to understand any inherent biases in the data that the model has picked up. When the chosen model is biased toward protected classes, you can use Responsible AI Toolbox for model mitigation.
 
 1. AI / machine learning - inference
 
    *Machine learning inference* is the process of feeding previously unseen data points into a machine learning model to calculate an output, like a numerical score. In this case, it's used to determine risks to patients.
- 
+
    The model registry is built into Azure Machine Learning. It's used to store and version models in the Azure cloud. The model registry makes it easy to organize and keep track of trained models.
 
    A trained machine learning model needs to be deployed so that new data can be fed through it for inferencing. The recommended deployment target is an [Azure managed endpoint](/azure/machine-learning/concept-endpoints).
@@ -85,11 +85,11 @@ Advancements in data collection technologies and developments in data standards
 
 Risk stratification can use either a binary or a multiclass classification model. In the case of binary classification, outcomes are a surgery resulting in either a successful or a risky outcome. In the multiclass classification approach, there's an opportunity to further refine outcomes as successful, moderate, or severe/death. For either approach, you need patient-centric data, including demographic information, comorbidities, current medication plan, blood test reports, and anything else that can shed light on a patient's overall health.
 
-Developing a transparent system that provides the ability to explain potential surgical outcomes to a patient must be the primary goal of models like this one. Transparency and interpretability help clinicians to have meaningful conversations with patients and lets them establish a treatment plan before surgery takes place. 
+Developing a transparent system that provides the ability to explain potential surgical outcomes to a patient must be the primary goal of models like this one. Transparency and interpretability help clinicians to have meaningful conversations with patients and lets them establish a treatment plan before surgery takes place.
 
 It's also important to acknowledge that patients come from diverse backgrounds. You need to create a model that's free from bias toward protected classes like gender and race. An unbiased model provides unbiased medical support for patients, irrespective of their backgrounds, to maximize their chances of a positive surgical outcome. The architecture in this article uses interpretability and bias-detection tools from the Responsible AI Toolbox.
 
-One of the largest healthcare organizations in the world, National Health Services in the United Kingdom, uses the Azure machine learning platform and the Responsible AI Toolbox for risk stratification models for orthopedic surgery. For more information, see [Two NHS surgeons are using Azure AI to spot patients facing increased risks during surgery](https://news.microsoft.com/en-gb/features/two-nhs-surgeons-are-using-azure-ai-to-spot-patients-facing-increased-risks-during-surgery).
+One of the largest healthcare organizations in the world, National Health Services in the United Kingdom, uses the Azure Machine Learning platform and the Responsible AI Toolbox for risk stratification models for orthopedic surgery. For more information, see [Two NHS surgeons are using Azure AI to spot patients facing increased risks during surgery](https://news.microsoft.com/en-gb/features/two-nhs-surgeons-are-using-azure-ai-to-spot-patients-facing-increased-risks-during-surgery).
 
 Or watch this short video:
 
@@ -151,7 +151,7 @@ Operational excellence covers the operations processes that deploy an applicatio
 
 Follow MLOps guidelines to standardize and manage an end-to-end machine learning lifecycle that's scalable across multiple workspaces. Before going into production, ensure that the implemented solution supports ongoing inference with retraining cycles and automated redeployments of models. Here are some resources to consider:
 
-- [MLOps v2](/azure/machine-learning/concept-model-management-and-deployment?view=azureml-api-2) 
+- [MLOps v2](/azure/machine-learning/concept-model-management-and-deployment?view=azureml-api-2)
 - [Azure MLOps (v2) solution accelerator](https://github.com/Azure/mlops-v2)
 
 Responsible AI as a part of Azure Machine Learning is based on the six pillars of AI development and use: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. For an overview and implementation details, see [What is responsible AI?](/azure/machine-learning/concept-responsible-ml).