This article is contributed. See the original author and article here.
Introduction
AI, without a doubt, is revolutionizing low code development. The capabilities of Artificial Intelligence into Low code have the power to revolutionize the way you work and enhance the applications and solutions you build.
You may be wondering what’s in it for you with AI as a low code developer. Well, AI has immense potential from automating repetitive tasks, adding intelligence into your applications, building chatbots, automated workflows, predictive analysis and much more on AI.
As a low code developer, you understand the power of technology to streamline the development to deployment process. Well in addition, with the recent development of AI this is a chance to take your skills to the next level. This is a rapidly growing field with massive impact and as a low code developer, you certainly do not need ten years of experience to develop AI models or rather add intelligence into your solution. In this blog, we’ll explore the basics of AI for low code developers, what opportunities you have in this platform and responsible AI.
What is Artificial Intelligence?
AI refers to the development of algorithms that can perform tasks that typically require human intelligence such as recognition, decision-making, solving problems and cognitive services. This usually involves training a computer/model to recognize patterns, make decisions and solve problems based on data. With the current development of AI, the main goal is to be able to create systems that can learn, adapt, and improve over time.
The results of AI are immense and have the potential to revolutionize many industries and change the way we live and work. For a low code developer this means you can automate tasks, improve accuracy and speed, provide valuable insights that can enhance user experience.
Opportunity of AI for a low code developer.
As a low code developer, the opportunity to integrate AI into your development process is too good to ignore. Regardless of your level of experience as a low code developer, AI is a powerful tool that can help you add intelligence into your solution and get the most out of it. As AI continues to evolve, we can expect to see more innovative solutions and use cases of AI in our solutions. Some examples of the several ways you can use AI as a low code developer include:
Creating chatbots – Leveraging Power Virtual Agents helps you create conversational chatbots where customers can get quicker services from your business with tasks such as customer service, ticket processing and general inquiry. It is easier also to integrate chatbots solutions into your existing solutions easily. For instance, once you are done building your solution you can publish the chatbot onto your website, mobile apps, messaging platforms (teams, Facebook). Get started here Intelligent Virtual Agents and Bots | Microsoft Power Virtual Agents
Top tip: Remember to publish your chatbot for any updates you make to reflect changes.
Automated workflows – AI can be used to automate workflows in low code applications, reducing manual effort and improving efficiency. This also helps an organization follow a structured manner in the business processes with the organization.
Decision making – Using AI you can be able to gain some valuable insights from the data that you have. For instance, if you need to predict a future trend based on data from the past few years, you can achieve this as a low code developer using AI
Image and object detection – Leveraging object detection allows you to fully utilize AI as a low code developer from tasks such as document scanning and extraction of texts from images. This will help you improve the quality of your applications and extract substantial amounts of data in a short time.
Natural language processing – This can be used in low code applications to improve accuracy and speed of text- based tasks such as sentiment analysis. If you need to detect the tone in a customer service review, you can leverage on this to detect the sentiments whether it is positive, neutral, or negative.
Here are some of the key benefits of using AI in low code development.
Automation of repetitive tasks – With AI, you can automate repeated tasks such as data entry, form processing and this will let you focus on other high priority business activities
Improved accuracy and speed – Some processes that are tedious and manual can be prone to errors and time consuming. Pre-built AI Models can be integrated into your solutions to enhance your application’s accuracy and speed.
Gaining valuable insights – AI can help low code developers extract valuable insights from data such as trends, this helps you make data-driven decisions for greater business success.
Getting started with AI as a Low code developer.
You can quickly bring AI into your solutions using Microsoft Power Platform, connecting to your business data regardless of where they are stored in One Drive, SharePoint, Azure, Dataverse
With a few simple steps you can easily get started using AI Builder
.
Understand business requirements – Before you begin, it is important to understand the business process that needs to be integrated with AI.
Choose a use case – Once you have decided on the business process that needs AI choose the best use case you can start with. Some of the common use cases we have are similar across businesses, including form processing, invoice processing and sentiment analysis. If the business problem is unique to your business, you can create a model from scratch to better suit your business.
Using AI Builder – To use AI Builder, you need to have a Microsoft Power Apps or Power Automate license, with which you can access AI Builder from your Power App or flow. If you don’t have one, you can create a free developers account here
Choosing an AI Model – You can either use a pre-built template or create a custom model from scratch using your own data or pre-existing data. Using a prebuilt model means that you are utilizing already built AI scenario templates, like invoice processing.
Test and deploy your model – Once you have selected the model that you want to use based on the business process case, with AI builder, you can test the model’s performance before deployment. With this you can validate the model’s accuracy and adjust it as needed. Once you are satisfied you can deploy it to your Power Apps application or Power Automate flow.
Monitoring and Improvement – After deploying your model, you can monitor its performance and adjust as per your need.
Responsible AI
As you get started with AI as a low code developer, it is important to ensure that the AI you build is developed and used for the right purpose and in the right environment. Microsoft highlights six key principles for responsible AI namely:
Transparency
Fairness
Reliability and safety
Privacy and security
Inclusiveness
Accountability.
To achieve this as a low code developer who is exploring AI, Microsoft provides resources to help the developers, businesses, and individuals to understand and use responsible AI practices and ethics. This provides a set of principles to guide the development of AI in a responsible way.
This article is contributed. See the original author and article here.
Preparando-se para o exame DP-900 Microsoft Azure Data Fundamentals e não sabe por onde começar? Este artigo é um guia de estudo para certificação DP-900!
Fiz uma curadoria de artigos da Microsoft para cada objetivo do exame DP-900. Além disso, compartilho os conteúdos da #SprintDP900, uma série de mentorias do Microsoft Reactor.
No Microsoft Reactor, oferecemos diversos conteúdos gratuitos de capacitação em tecnologias da Microsoft e organizamos sprints de estudos para certificações. Na #SprintDP900, estamos realizando uma série de 3 aulas sobre certificação Azure Data Fundamentals, nos dias 28 de fevereiro, 01 e 02 de março. Todas as pessoas que participarem do Cloud Skills Challenge e assistirem as aulas, poderão participar do quiz de avaliação de conhecimentos e concorrer a um voucher gratuito para realização da prova.
Agenda #SprintDP900
28 de fevereiro, às 12:30h #SprintDP900: Introdução à Banco de dados no Azure e tipos de serviços
No primeiro encontro, você irá aprender sobre os conceitos básicos de banco de dados na nuvem, entendo cargas de trabalho, funções e serviços comuns.
As gravações das aulas estarão disponíveis em nosso canal, basta acessar o link de cada sessão.
O Microsoft Cloud Skills Challenge é uma plataforma integrada com o Microsoft Learn, que é uma plataforma global, disponível 24 horas por dia, 7 dias por semana. Você pode criar sua agenda de estudos, pois o desafio estará disponíveis no período de 28/02/2023 a 10/02/2023. As aulas semanais ocorrem no formato ao vivo e se você não puder participar, terá a possibilidade de assistir as gravações.
O que eu preciso fazer para ganhar um voucher de certificação?
Você deverá realizar sua inscrição para as aulas ao vivo e realizar a trilha de estudos proposta no Cloud Skills Challenge. Na aula que será realizada no dia 02 de março, vamos disponibilizar um quiz de validação de conhecimentos para selecionar as 100 pessoas que receberão, por e-mail, um voucher gratuito para realização da certificação DP-900: Azure Data Fundamentals. O critério de priorização dos vouchers é a conclusão do Cloud Skills Challenge, participação nas aulas e obtenção de 80% de acerto no quiz.
Recursos adicionais de aprendizado
Se você não tem muita familiaridade com computação em nuvem, recomendo estudar a trilhaAzure Fundamentals:
This article is contributed. See the original author and article here.
Microsoft Purview Data Catalog provides data scientists, engineers, and analysts with the data they need for BI, analytics, AI, and machine learning. It makes data easily discoverable by using familiar business and technical search terms and eliminates the need for Excel data dictionaries with an enterprise-grade business glossary. It enables customers to track the origin of their data with interactive data lineage visualization.
We continue to listen to your feedback and have been hard at work to enable various features in Purview Data Catalog in different areas like data curation, browse & search, business glossaries, business workflows, and self-service data access among others in the last 6 months.
Data Curation:
Create, update, delete, and assign managed attributes to data assets. Learn more here.
Rich text editor support for asset updates (description etc.). Learn more here.
Browse & Search:
Keyword highlighting in search results. Learn more here.
Managed attributes filter support in search results. Learn more here.
Business Glossary:
Create multiple glossaries to manage business terms across different business units in your organization. Learn more here.
Rich text editor support for business glossaries. Learn more here.
Delete term templates without references. Learn more here.
Add, update, and remove templates for existing terms. Learn more here.
Business Workflows:
Approval workflow for data asset curation. Learn more here.
Set reminders and expiry for approvals and task requests in workflows. Learn more here.
Self-Service Data Access:
Request access on behalf of another user in Microsoft Purview Studio. Learn more here.
Request access on behalf of another user in Microsoft Synapse Studio. Learn more here.
Assign data asset owners as approvers for self-service data access. Learn more here.
Our goal is to continue adding features and improve the usability of Microsoft Purview governance capabilities. Get Started easily and quickly using Microsoft Purview. If you have any feature requests or want to provide feedback, please visit the Microsoft Purview forum.
This article is contributed. See the original author and article here.
As more and more industries are digitizing their operations, there is a need for simulation to enable these digital transformations. Simulation helps industries meet their business and operations goals by changing the environment variables and predicting the outcomes.
Azure Digital Twins (ADT) is a powerful way of simulating changes in the real world to reduce costs and operational overhead. For example, a manufacturing factory can have a representation in Azure Digital Twins, and customers can use the digital representation to observe its operations with the existing setup. However, if customers want to simulate changes and compare the cost of operation, quality of product, or time taken to build a product, they could use ADT to tweak their digital representations’ models, properties, and to observe the impact of these changes on the simulation.
Azure Digital Twins already supports APIs to create new models, twins, and relationships. But now, with the public preview release of the Jobs API, you can ingest large twin graphs into Azure Digital Twins with enriched logging and higher throughput. This in turn enables simulation scenarios, faster setup of new instances, and automate the model and import workflows for customers. It eliminates the need for multiple API requests to ingest a large twin graph, and the need for handling errors and retries across these multiple requests.
What’s new with the Jobs API?
Quickly populate an Azure Digital Twins instance: Import twins and relationships at a much faster rate than our existing APIs. Typically, the Jobs API allows import of:
1M twins in about 10 mins, and 1M relationships in about 15 mins.
12M entities consisting of 4M twins and 8M relationships in 90 to 120 mins.
12M entities consisting of 1M twins and 11M relationships in 135 to 180 mins, where most twins have 10 relationships, and 20 twins have 50k relationships. Note: TheJobs API for import today scales out for performance, based on the usage pattern of the customer. The numbers shown above take the time for this auto scale into account.
Ingestion Limits: Import up to 2M twins and 10M relationships in one import job.
Structured Output logs: The Jobs API produces structured and informative output logs indicating job state, progress, and more detailed error messages with line numbers.
Metrics: Additional metrics for your ADT instance indicating the number of entities ingested through import jobs are now available in the Azure portal.
RBAC (role-based access control): The built-in role that provides all of these permissions is Azure Digital Twins Data Owner. You can also use a custom role to grant granular access to only the data types that you need.
Same billing model for public preview: The billing model for the Jobs API matches the existing billing for models/twins APIs. The import of entities is equivalent to create operations in Azure Digital Twins.
Import Job Workflow
Here are the steps to execute an import job.
The user creates a data file in the ndjson format containing models, twins, and relationships. We have a code sample that you can use to convert existing models, twins, and relationships into the ndjson format. This code is written for .NET and can be downloaded or adapted to help you create your own import files.
The user copies this data file to an Azure Blob Storage container.
The user specifies permissions for the input storage container and output storage container.
The user creates an import job, specifying the storage location of the file (input), as well as a storage location for error and log information (output). User also provides the name of the output log file. The service will automatically create the output blob to store progress logs. There are two ways of scheduling and executing import of a twin graph using the Jobs API:
This article is contributed. See the original author and article here.
We hope you will join us on Tuesday, March 7th to learn how to build intelligent, scalable apps faster and easier at this deep dive into open source and Azure. See the latest open-source technology in action—while connecting with the community of industry leaders, innovators, and open-source enthusiasts.
See app-building demos using Azure and the latest in open-source technologies, cloud-native architectures, and microservices.
Get tips and best practices for open source from industry experts at companies like HashiCorp, GitHub, and Redis.
Learn to build cloud-native apps for relational and nonrelational data with Azure Cosmos DB, now supporting native PostgreSQL.
Discover new capabilities in IaaS, PaaS, containers, and serverless computing, including Azure Kubernetes Service (AKS).
Explore practical ways to optimize your open-source investments and gain more time for innovation.
Learn how to protect your data and business assets by building on a highly secure cloud platform designed to meet your open-source security and compliance needs.
Plus, ask your questions during the live chat Q&A.
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