Blogs by Ashwin

  Overview Ensuring the security of Azure AI services can assist in preventing data breaches and violations of user privacy related to the data involved in the solution. Azure AI services offer several layers of security that should be taken into account when developing a solution. Consider Authentication By default, access to resources within Azure AI services is limited through the use of subscription keys. Managing access to these keys is a crucial aspect of ensuring security. Regenerate Keys Regularly updating keys is essential to mitigate the risk of unauthorized users gaining access to or sharing those keys. Key regeneration can be performed through the Azure portal or by using the Azure command-line interface (CLI) command az cognitiveservices account keys regenerate. Each AI service is provided with two keys, enabling you to regenerate keys without service interruption. To accomplish this: If you're using both keys in production, change your code so that only one key is in...

  Identify Endpoints and Keys An endpoint that allows an application to use the service is defined when you provision an Azure AI services service resource in your Azure subscription. To consume the service through the endpoint, applications require the following information: The Endpoint URI - This is the HTTP address at which the REST interface for the service can be accessed. Most AI services software development kits (SDKs) use the endpoint URI to initiate a connection to the endpoint. A Subscription Key - Access to the endpoint is restricted based on a subscription key. Client applications must provide a valid key to consume the service. When you provision an AI services resource, two keys are created and applications can use either key. You can also regenerate the keys as required to control access to your resource. The Resource Location - When you provision a resource in Azure, you generally assign it to a location, which determines the Azure data center in which the resourc...

  About Azure AI Services is a set of services that you can use into your apps as building blocks of AI capabilities. Cloud-based services that incorporate AI capabilities are called Azure AI services. You should consider AI services as a collection of separate services that you can use as building blocks to create complex, intelligent applications rather than as a single solution. Azure AI services include: Azure AI Document Intelligence - An Optical Character Recognition (OCR) solution that can extract semantic meaning from forms, such as invoices, receipts, and others. Azure AI Immersive Reader - A reading solution that supports people of all ages and abilities. Azure AI Search - A cloud-scale search solution that uses AI services to extract insights from data and documents. Azure OpenAI - An Azure Cognitive Service that provides access to the capabilities of OpenAI GPT-4. Provision an Azure AI Services Resource Azure AI services include a wide range of AI capabilities that ...

  Understand Capabilities Of Azure OpenAI Service Generative AI is a relatively recent and rapidly advancing area of artificial intelligence that centers on AI systems that create content. The content produced by these systems can take various forms, including text, images, code, and more, effectively simulating interactions akin to conversing with a genuine person. These generative AI systems rely on large language models (LLMs) built on the transformer architecture, which has developed from years of advancements in machine learning. Typically, users interact with generative AI models using natural language prompts, and when asked correctly, these models provide notably accurate responses. An Azure AI service for installing, using, and optimizing OpenAI models is called Azure OpenAI Service. One of the most widely used apps that most people have seen is ChatGPT, which was developed by OpenAI. The models that ChatGPT utilizes are accessible through the Azure OpenAI comprehensive so...

  Understand capabilities of Azure Machine Learning Microsoft Azure offers the Azure Machine Learning service, which is a cloud-based platform designed to conduct experiments at scale for training predictive models from data and to deploy the trained models as services. Azure Machine Learning provides the following features and capabilities: Automated machine learning- This feature enables non-experts to quickly create an effective machine learning model from data. Azure Machine Learning designer- It’s a graphical interface enabling no-code development of machine learning solutions. Data and compute management- It’s a Cloud-based data storage and compute resources that professional data scientists can use to run data experiment code at scale. Pipelines- Data scientists, software engineers, and IT operations professionals can define pipelines to orchestrate model training, deployment, and management tasks. Data scientists can use Azure Machine Learning throughout the entire ma...

  Understand Considerations For Responsible AI Some core principles for responsible AI that have been adopted at Microsoft are given below: Fairness Everyone should be treated equally by AI systems. Research on machine learning systems' fairness is quite active, and there are software tools available for assessing, measuring, and reducing unfairness in machine learning models. But tooling is insufficient on its own to guarantee fairness. From the start of the application development process, take fairness into account by carefully examining training data to make sure it is representative of all possibly impacted subjects and assessing prediction performance for your user population over the course of development. Reliability and Safety AI systems must function safely and reliably. Before being released, AI-based software applications must undergo stringent testing and deployment management procedures, just like any other software. Software engineers must also consider the probabili...