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Provision An Azure AI Services Resource

By Ashwin Venugopal -
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  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 ...
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Capabilities Of Azure OpenAI Service & Azure AI Search

By Ashwin Venugopal -
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  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...
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Capabilities Of Azure Machine Learning

By Ashwin Venugopal -
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  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...
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Prepare To Develop AI Solutions On Azure (Part 2)

By Ashwin Venugopal -
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  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...
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Prepare To Develop AI Solutions On Azure (Part 1)

By Ashwin Venugopal -
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  Model Training and Inferencing Numerous AI systems depend on predictive models that require training with sample data. During the training phase, the data is examined to identify connections between the features in the dataset (the values that are likely to appear in future observations) and the label (the value that the model is designed to forecast). Once the model is trained, you can provide new data with known feature values for the model to predict the most probable label. The process of using the model to generate predictions is known as inferencing. Numerous services and frameworks available for software engineers to create AI-enabled solutions necessitate a development process that includes training a model with existing data before it can be applied to infer new values in an application. Probability and Confidence Scores No predictive model is perfect, however a well-trained machine learning model can be accurate. Although software engineers don't need a thorough mathema...
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Getting Started With Azure AI Services

By Ashwin Venugopal -
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  What is Artificial Intelligence (AI)? In general terms, AI can be considered as a software that exhibits one or more human-like capabilities described below: Visual Perception- It is the ability to use computer vision capabilities to accept, interpret, and process input from images, video streams, and live cameras. Text Analysis and Conversation- It is the ability to use Natural Language Processing (NLP) to not only "read", but also generate realistic responses and extract semantic meaning from text.  Speech- It is the ability to recognize speech as input and synthesize spoken output.  Decision Making- It is the ability to use past experiences and learned correlations to assess situations and take appropriate actions.  These kind of capabilities are increasingly within the reach of everyday software applications making them more useful in a wide variety of scenarios. AI-related Terms It is necessary to understand clear definitions of other AI related terms. Data Sc...
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Publishing & Sharing Your Power App

By Ashwin Venugopal -
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  Introduction When your Power App is ready, use the save, publish and share icons. Your workflow for publishing for the first time would be: Build your app Save your changes Click Publish (to make this the version that people will use) Share (select who can access the app or view it) A final step to take if you have linked your app to data is to make sure the data is also shared with your intended group or person. After completing this step, people will have the opportunity to engage with and utilize the app you have developed. An important tip is to choose a descriptive name, design an appealing icon, and provide a brief overview in the description. Power Apps Mobile App In order to use a Power App that you have developed on your mobile device, you must download the Power Apps app from the Google Play Store (for Android devices) or the Apple App Store (for iOS devices). The first time you start the Power Apps app, it will ask you to sign into your organizational account...
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