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Object detection is a form of computer vision in which a model is trained to detect the presence and location of one or more classes of object in an image.

There are two components to an object detection prediction:

• The class label of each object detected in the image. For example, you might ascertain that an image contains an apple, an orange, and a banana.
• The location of each object within the image, indicated as coordinates of a bounding box that encloses the object.

To train an object detection model, you can use the Azure AI Custom Vision portal to upload and label images before training, evaluating, testing, and publishing the model; or you can use the REST API or a language-specific SDK to write code that performs the training tasks.

Image labeling

You can use Azure AI Custom Vision to create projects for image classification or object detection. The most significant difference between training an image classification model and training an object detection model is the labeling of the images with tags. While image classification requires one or more tags that apply to the whole image, object detection requires that each label consists of a tag and a region that defines the bounding box for each object in an image.

Labeling images in the Azure AI Custom Vision portal

The Azure AI Custom Vision portal provides a graphical interface that you can use to label your training images.

The easiest option for labeling images for object detection is to use the interactive interface in the Azure AI Custom Vision portal. This interface automatically suggests regions that contain objects, to which you can assign tags or adjust by dragging the bounding box to enclose the object you want to label.

Additionally, after tagging an initial batch of images, you can train the model. Subsequent labeling of new images can benefit from the smart labeler tool in the portal, which can suggest not only the regions, but the classes of object they contain.

application services

To use the Custom Vision service to create an object detection solution, you need two Custom Vision resources in your Azure subscription:

• An Azure AI Custom Vision training resource – used to train a custom model based on your own training images.
• An Azure AI Custom Vision prediction resource – used to generate predictions from new images based on your trained model.

When you provision the Azure AI Custom Vision service in an Azure subscription, you can choose to create one or both of these resources. This separation of training and prediction provides flexibility. For example, you can use a training resource in one region to train your model using your own image data; and then deploy one or more prediction resources in other regions to support computer vision applications that need to use your model.

Each resource has its own unique endpoint and authentication keys; which are used by client applications to connect and authenticate to the service.

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Microsoft Fabric’s Warehouse provides integrated data ingestion tools, enabling users to load and ingest data into warehouses on a large scale through either coding or noncoding experiences.

Data pipeline is the cloud-based service for data integration, which enables the creation of workflows for data movement and data transformation at scale. You can create and schedule data pipelines that can ingest and load data from disparate data stores. You can build complex ETL, or ELT processes that transform data visually with data flows.

Most of the functionality of data pipelines in Microsoft Fabric comes from Azure Data Factory, allowing for seamless integration and utilization of its features within the Microsoft Fabric ecosystem.

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Each organization has their own guiding principles, but ultimately these principles need to be part of a larger responsible AI strategy to be effective. This strategy should encompass how your organization brings these principles to life both within your organization and beyond.

We recommend establishing a governance system that is tailored to your organization’s unique characteristics, culture, guiding principles, and level of engagement with AI. The tasks of the board should include designing responsible AI policies and measures; attending they’re being followed, and ensuring compliance.

To help your organization get started, we have provided an overview of three common governance approaches: hiring a Chief Ethics Officer, establishing an ethics office, and forming an ethics committee. The first approach is centralized, and the others are decentralized. All of them have their benefits, but we recommend combining them in a hybrid approach. A governance system that reports to the board of directors and has financial support, human resources, and authority is more likely to create real change across an organization.

design

To build trust, it’s critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions. These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation. It’s also important to be able to verify that these systems are behaving as intended under actual operating conditions. How they behave and the variety of conditions they can handle reliably and safely largely reflects the range of situations and circumstances that developers anticipate during design and testing.

To ensure reliability and safety in your AI system, you should:

• Develop processes for auditing AI systems to evaluate the quality and suitability of data and models, monitor ongoing performance, and verify that systems are behaving as intended based on established performance measures.
• Provide detailed explanation of system operation including design specifications, information about training data, training failures that occurred and potential inadequacies with training data, and the inferences and significant predictions generated.
• Design for unintended circumstances such as accidental system interactions, the introduction of malicious data, or cyberattacks.
• Involve domain experts in the design and implementation processes, especially when using AI to help make consequential decisions about people.
• Conduct rigorous testing during AI system development and deployment to ensure that systems can respond safely to unanticipated circumstances, don’t have unexpected performance failures, and don’t evolve in unexpected ways. AI systems involved in high-stakes scenarios that affect human safety or large populations should be tested both in lab and real-world scenarios.
• Evaluate when and how an AI system should seek human input for impactful decisions or during critical situations. Consider how an AI system should transfer control to a human in a manner that is meaningful and intelligible. Design AI systems to ensure humans have the necessary level of input on highly impactful decisions.
• Develop a robust feedback mechanism for users to report performance issues so that you can resolve them quickly.

Privacy and security

As AI becomes more prevalent, protecting privacy and securing important personal and business information is becoming more critical and complex. With AI, privacy and data security issues require especially close attention because access to data is essential for AI systems to make accurate and informed predictions and decisions about people.

To ensure privacy and security in your AI system, you should:

• Comply with relevant data protection, privacy, and transparency laws by investing resources in developing compliance technologies and processes or working with a technology leader during the development of AI systems. Develop processes to continually check that the AI systems are satisfying all aspects of these laws.
• Design AI systems to maintain the integrity of personal data so that they can only use personal data during the time it’s required and for the defined purposes that have been shared with customers. Delete inadvertently collected personal data or data that is no longer relevant to the defined purpose.
• Protect AI systems from bad actors by designing AI systems in accordance with secure development and operations foundations, using role-based access, and protecting personal and confidential data that is transferred to third parties. Design AI systems to identify abnormal behaviors and to prevent manipulation and malicious attacks.
• Design AI systems with appropriate controls for customers to make choices about how and why their data is collected and used.
• Ensure your AI system maintains anonymity by taking into account how the system removes personal identification from data.
• Conduct privacy and security reviews for all AI systems.
• Research and implement industry best practices for tracking relevant information about customer data, accessing and using that data, and auditing access and use.

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In the last unit, we discussed some of the societal implications of AI. We touched on the responsibility of businesses, governments, NGOs, and academic researchers to anticipate and mitigate unintended consequences of AI technology. As organizations consider these responsibilities, more are creating internal policies and practices to guide their AI efforts.

At Microsoft, we’ve recognized six principles that we believe should guide AI development and use: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. For us, these principles are the cornerstone of a responsible and trustworthy approach to AI, especially as intelligent technology becomes more prevalent in the products and services we use every day.

Fairness

AI systems should treat everyone fairly and avoid affecting similarly situated groups of people in different ways. For example, when AI systems provide guidance on medical treatment, loan applications, or employment, they should make the same recommendations to everyone with similar symptoms, financial circumstances, or professional qualifications.

To ensure fairness in your AI system, you should:

• Understand the scope, spirit, and potential uses of the AI system by asking questions such as, how is the system intended to work? Who is the system designed to work for? Will the system work for everyone equally? How can it harm others?
• Attract a diverse pool of talent. Ensure the design team reflects the world in which we live by including team members that have different backgrounds, experiences, education, and perspectives.
• Identify bias in datasets by evaluating where the data came from, understanding how it was organized, and testing to ensure it’s represented. Bias can be introduced at every stage in creation, from collection to modeling to operation. The Responsible AI Dashboard, available at the Resources section, includes a feature to help with this task.
• Identify bias in machine learning algorithms by applying tools and techniques that improve the transparency and intelligibility of models. Users should actively identify and remove bias in machine learning algorithms.
• Leverage human review and domain expertise. Train employees to understand the meaning and implications of AI results, especially when AI is used to inform consequential decisions about people. Decisions that use AI should always be paired with human review. Include relevant subject matter experts in the design process and in deployment decisions. An example would be including a consumer credit subject matter expert for a credit scoring AI system. You should use AI as a copilot, that is, an assisting tool that helps you do your job better and faster but requires some degree of supervising.
• Research and employ best practices, analytical techniques, and tools from other institutions and enterprises to help detect, prevent, and address bias in AI systems.

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It’s important to recognize that as new intelligent technology emerges and proliferates throughout society, with its benefits come unintended and unforeseen consequences. Some of these consequences have significant ethical ramifications and the potential to cause serious harm. While organizations can’t predict the future yet, it’s our responsibility to make a concerted effort to anticipate and mitigate the unintended consequences of the technology we release into the world through deliberate planning and continual oversight.

Threats

Each breakthrough in AI technologies brings a new reminder of our shared responsibility. For example, in 2016, Microsoft released a chatbot on X called Tay, which could learn from interactions with X users. The goal was to enable the chatbot to better replicate human communication and personality traits. However, within 24 hours, users realized that the chatbot could learn from bigoted rhetoric, and turned the chatbot into a vehicle for hate speech. This experience is one example of why we must consider human threats when designing AI systems.

Novel threats require a constant evolution in our approach to responsible AI. For example, because generative AI enables people to create or edit videos, images, or audio files so credibly that they look real, media authenticity is harder to verify. In response, Microsoft is teaming with other technology and news stakeholders to develop technical standards to address deepfake-related manipulation.

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AI is the defining technology of our time. It’s already enabling faster and more profound progress in nearly every field of human endeavor and helping to address some of society’s most daunting challenges. For example, AI can help people with visual disabilities understand images by generating descriptive text for images. In another example, AI can help farmers produce enough food for the growing global population.

At Microsoft, we believe that the computational intelligence of AI should be used to amplify the innate creativity and ingenuity of humans. Our vision for AI is to empower every developer to innovate, empower organizations to transform industries, and empower people to transform society.

Societal implications of AI

As with all great technological innovations in the past, the use of AI technology has broad impacts on society, raising complex and challenging questions about the future we want to see. AI has implications on decision-making across industries, data security and privacy, and the skills people need to succeed in the workplace. As we look to this future, we must ask ourselves:

• How do we design, build, and use AI systems that create a positive impact on individuals and society?
• How can we best prepare workers for the effects of AI?
• How can we attain the benefits of AI while respecting privacy?

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Security and monitoring are critical aspects of managing your data warehouse.

Security

Data warehouse security is important to protect your data from unauthorized access. Fabric provides a number of security features to help you secure your data warehouse. These include:

• Role-based access control (RBAC) to control access to the warehouse and its data.
• TLS encryption to secure the communication between the warehouse and the client applications.
• Azure Storage Service Encryption to protect the data in transit and at rest.
• Azure Monitor and Azure Log Analytics to monitor the warehouse activity and audit the access to the data.
• Multifactor authentication (MFA) to add an extra layer of security to user accounts.
• Microsoft Entra ID integration to manage the user identities and access to the warehouse.

Workspace permissions

Data in Fabric is organized into workspaces, which are used to control access and manage the lifecycle of data and services. Appropriate workspace roles are the first line of defense in securing your data warehouse.

In addition to workspace roles, you can grant item permissions and access through SQL.

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Now that you know how to implement a data warehouse in Fabric, let’s prepare the data for analytics.

There are two ways to query data from your data warehouse. The Visual query editor provides a no-code, drag-and-drop experience to create your queries. If you’re comfortable with T-SQL, you may prefer to use the SQL query editor to write your queries. In both cases, you can create tables, views, and stored procedures to query data in the data warehouse and Lakehouse.

There’s also a SQL analytics endpoint, where you can connect from any tool.

Query data using the SQL query editor

The SQL query editor provides a query experience that includes intellisense, code completion, syntax highlighting, client-side parsing, and validation. If you’ve written T-SQL in SQL Server Management Studio (SSMS) or Azure Data Studio (ADS), you’ll find it familiar.

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