AI, AI/ML, Artificial Intelligence, Artificial Intelligence and Machine Learning

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How to Use AI Builder in Microsoft Power Platform?

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Introduction

In the modern digital era, businesses always look for fresh innovations to help them adapt more quickly to changing consumer demands. Low-code development is an emerging technology that has gained popularity and importance as it helps organizations to rapidly and efficiently accomplish their objectives. The Microsoft Power Platform is the leading name in Low code, No code approaches, transforming ideas into impactful sessions. The add-on to the Power Platform being low code no code is the AI (Artificial Intelligence) Builder feature, which offers AI models intended to streamline the company’s operations and processes. With the aid of AI Builder, the organization may apply intelligence to the processes and extract knowledge from data using Power Apps and Power Automate.

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Why AI Builder?

Leveraging the power of AI without having any coding or data science skills is simple while using AI Builder. One can either choose a prebuilt model that is prepared to use for many common business scenarios or can develop custom models that are tailored to the organization’s specific needs.

Integrating Power Apps and Power Automate gives a wide range of options to explore AI features.

The process of adding intelligence is simple –

  • Choose a model type based on the requirements of the business. Pick from a growing variety of AI solutions.
  • As a part of the Data connection stage, Choose the company-specific data from the list.
  • One can modify custom models based on the kind of model required to improve the performance of the AI.
  • The training process is automated based on the business data and customization, and it trains the AI model on how to solve the business problem ex: Prediction, and Sentiment analysis.
  • The AI model can produce insights after training, such as the prediction outcome or identifying the sentiment behind the statement fed.

The essential step-by-step approach in any AI model includes, Build, Train, Manage, Publish and finally, using the model.

AI Model Types

There are two types of AI models: 1. Prebuilt AI model 2. Custom AI model

Prebuilt AI model: The Prebuilt models from AI Builder make it easier to incorporate intelligence into apps and workflows without having to collect data, build, train, and publish your own models. Ex: Sentiment analysis, Language detection, Invoice Processing, and many more.

Ex1. The prebuilt sentiment analysis model determines whether text data has a positive or negative sentiment. It can analyze social media, client evaluations, or any other text data that stimulates your curiosity or interest.

Ex2. The prebuilt language detection model determines which language is used most frequently in a written document. The model examines the text and provides a number score between 0 and 1, along with the language found. Scores approaching 1 suggest a higher degree of confidence in the outcome.

Ex3. The prebuilt AI model for invoice processing captures important invoice data to aid in automating the processing of invoices. The model for processing invoices is designed to identify typical invoice components, including invoice ID, invoice date, and due date.

Custom AI model:  .

Ex1. The prediction models in AI Builder look for patterns in the historical data you supply. Prediction models pick up the ability to link those patterns to results. Then, we apply AI’s power to find previously learnt patterns in fresh data and utilize them to forecast future results.

Ex2. For businesses, the amount of text data is proliferating. Text data is being added in more and greater quantities through channels, including email, documents, and social media. When valuable information is taken after classification and applied, you can provide your clients better goods and services.

Ex3. When you process documents, you can read and store data from common papers like tax forms or invoices. You can save a lot of time by using Power Automate and Power Apps to evaluate, extract, organize, and save the data automatically during this process.

The world is continuously evolving because of Artificial Intelligence, and we are evolving with it. With these advancements, organizations can grow in more significant intensity with great business. Thus, we are opening a new horizon with the AI builder to simplify our work.

 

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CloudThat, incepted in 2012, is the first Indian organization to offer Cloud training and consultancy for mid-market and enterprise clients. Our business aims to provide global services on Cloud Engineering, Training, and Expert Line. Our expertise in all major cloud platforms, including Microsoft Azure, Amazon Web Services (AWS), VMware, and Google Cloud Platform (GCP), positions us as pioneers.

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FAQs

1. Define AI builder.

ANS: – An artificial intelligence (AI) builder is a platform or tool that enables people to develop and use AI models and applications without considerable coding or data science knowledge. It offers a user-friendly interface and pre-built components to simplify the process of creating and integrating AI into different applications.

2. What can be build using AI Builder?

ANS: – You can create various AI-powered apps using an AI builder, including chatbots, virtual assistants, recommendation engines, sentiment analysis tools, image recognition programs, predictive analytics models, and more.

3. Is there a need for me to have coding background to use AI Builder?

ANS: – No, using an AI builder typically doesn’t require a lot of programming skills. These platforms have a simple design and are open to people without much technological expertise. However, having a fundamental understanding of ideas like data input and output, machine learning, and AI principles can be helpful.

4. What is the process involved in AI builder work?

ANS: – Creating AI models and workflows is made possible by AI builders, who frequently offer a visual interface or drag-and-drop components. They consist of pre-built AI models you can customize to your unique requirements. The platforms often handle operations like model training, deployment, and data preprocessing, streamlining development.

WRITTEN BY Sushma Uday Kamat

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