Catalin Popa
Cloud Solutions Architect - Founder of adaptive.run
Bucharest, Romania
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Catalin Popa is an Azure Microsoft MVP and a leading expert in app development and cloud computing, with several years of experience working in international enterprise projects all around the world. As a cloud solutions architect, he helps organizations realize digital transformation by adopting cloud topics and best practices, having extensive understanding and experience in cloud solutions, from both business and technical aspects.
His company, adaptive.run, focuses solely on Azure technologies, having a wide portfolio of services and solutions based on AI and ML.
Area of Expertise
Topics
Full Azure infrastructure deployments via PowerShell
When it comes to infrastructure-as-code, the most used ways of deploying Azure infrastructure is via bicep, ARM templates or Terraform.
But what about PowerShell? is it a feasible option for infrastructure deployment and configuration? Let's talk about the pros and cons of deploying and configuring Azure resources via PowerShell while we demonstrate how it's done.
One-Click deployments with ARM templates and Azure DevOps
Deploying full solutions into the cloud has always been tricky. Creating and managing different cloud resources, so that the solution will be up and running can create a lot of work. In this session, we will demonstrate how such deployments can be automated and be performed at scalable level. Have your cloud application/environment up and running with just one click of a button/execution of a script with ARM templates and Azure DevOps.
Spoken or written speech insights with AI and Azure Cognitive Services.
Join this session to learn how valuable data insights from text or audio can be extracted with the help Azure cloud, Artificial Intelligence and Cognitive Services.
We will demonstrate speech-to-text and text-to-speech capabilities, afterwards performing the sentiment analysis on the conversation and labeling it as either positive, neutral or negative.
This session will demonstrate the development of a simple app which will analyze an audio file that represents a phone conversation, will extract the full text transcript from it and will analyze if the conversation was in a positive or negative tone.
Automating data extraction from invoices with Azure Form Recognizer
Data processing for invoices is a hassle for any company. Financial teams have to perform this process each month - and this is usually done manually. And you know what that means - manual processing means a slow and prone to errors process.
Now, with the help of Artificial Intelligence, this can be automated, while the human-error can be reduced to near-zero.
Join this session to find out how, with the help of Azure Form Recognizer, data can be extracted in automated way and passed along to other 3rd party services.
In this session we will showcase an app which will train AI models to recognize invoices and certain fields from it so that data extraction will be made.
Azure Infra-as-code: ARM vs Terraform vs Bicep
When it comes to deploying Azure environments in an automated manner, infrastructure-as-code is the way to do it. And the most used ways to achieve it is via ARM templates, via Terraform or via Bicep.
But what are the differences between them? How do you know when to choose which?
Join this session and let's discuss the pros and cons of each, while we demonstrate them by deploying an Azure environment.
AI and IoT Bulgaria Summit 2021 Sessionize Event
DevOps Pro Europe 2021
Automated Infrastructure Deployment in Azure With Arm Templates and Azure Devops - Deploying full solutions into the cloud has always been tricky. Creating and managing different cloud resources, so that the solution will be up and running can create a lot of work. In this session, we will demonstrate how such deployments can be automated and be performed at scalable level. Have your cloud application/environment up and running with just one click of a button/execution of a script with ARM templates and Azure Devops.
Microsoft Online Webinars
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