Georgia Kalyva
Microsoft Technical Trainer @ Microsoft, MBA
Athens, Greece
Georgia Kalyva currently works as a Microsoft Technical Trainer at Microsoft. Before joining Microsoft, she was recognized as a Microsoft AI MVP, is a Microsoft Certified Trainer and an international speaker with years of expertise in Microsoft Cloud, AI and developer technologies. With a background in software development, leadership and technology, her career expands over several areas ranging from solutions design and implementation to project management and digital transformation. Having a Bachelor’s degree in Informatics from the University of Piraeus and a Master’s degree in Business Administration from the University of Derby, she is uniquely qualified to lead technology and innovation solutions in both internal and customer-facing roles. Georgia’s honors include several awards from international technology and business competitions and her journey to excellence stems from a growth mindset and a passion for technology.
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Create your own Healthcare Assistant
The Microsoft Healthcare Bot service empowers healthcare organizations to build and deploy an AI-powered, compliant, conversational healthcare digital agents. In this session, we are going to learn how to use the Health Bot service to create and publish our own Health Care bot scenario and extend it using LUIS and custom flows to tailor it to our needs.
Data Brokers and the Social Dilemma
Are we being tracked? Should we be worried? Who is behind all this? What can we do to protect ourselves?
The business value of integrating AI solutions
Every business is powered by applications, websites and systems designed to help organize, manage and monitor all the business processes essential for its well-being and continuous growth. Although this is the first step towards digital transformation, AI-powered solutions can provide a significant strategical advantage and assist in decision-making when planning for the future. In this session, we will see an overview of Azure AI and see how Microsoft tools and services can provide real business value, whether it is from unlocking insights or building intelligent apps.
The advantages of building Bots using the Bot Composer
In this session, we are going to see the advantages of building a bot using the Bot Framework Composer with minimal coding using a visual dialog and how to add language understanding (LUIS) and language generation features. Finally, we are going to deploy it using the Azure Bot service and see how to publish it to multiple channels (Messenger, Skype, Teams and the Web)
Make an Azure Help-desk Assistant Bot in 1, 2, 3
In this session we are going to use the Azure Bot Service together with Azure Cognitive services, to create your virtual help-desk assistant in 3 simple steps. First, we are going to learn how to create a knowledge base, then we are going to create a natural language model using LUIS.ai (Language Understanding Intelligent Service) and finally combine everything to create and deploy the bot to the Azure App Service, to be publicly available everywhere. Publish options include Skype,Teams and more.
Introduction to Azure Machine Learning Studio
In this session, we are going to see how to use the designer and the Azure ML SDKs and CLI to quickly prep data, train and deploy machine learning models. We will see how several Azure Machine Learning features work like multiple framework support and advanced ML capabilities like automated machine learning and pipeline support.
Introduction to AI and Azure Cognitive Services
What is Artificial Intelligence? How can I start building AI-enabled applications? What happens to the data I upload? Microsoft Cognitive Services allow every developer to incorporate AI into their application without any machine learning expertise. In this session, we are going to see an overview of Microsoft Cognitive Services and how we can build smart applications using the family of AI services and APIs available.
Getting started with LUIS.ai
LUIS.ai or Language Understanding Intelligent service helps you create applications that understand what your user wants using their own words. In this session, we will see how we can create our own LUIS model and add it to any application or bot.
Building responsible AI solutions
AI is lacking governance and AI bias is a global issue. In this talk, we are going to see how to develop your own responsible AI strategy and principles that put people first.
Create a virtual help-desk Assistant in 3 steps
In this session we are going to use the Azure Bot Service together with Azure Cognitive services, to create your virtual help-desk assistant in 3 simple steps. First, we are going to learn how to create a knowledge base, then we are going to create a natural language model using LUIS.ai (Language Understanding Intelligent Service) and finally combine everything to create and deploy the bot to the Azure App Service, to be publicly available everywhere. Publish options include Skype, Teams and more.
Previous knowledge of the Azure Bot Service and LUIS is not required, however, some knowledge of ASP.NET Core and C# will be helpful for participation.
Harness the power of Azure Computer Vision
Computer Vision is the area of AI that deals with visual processing. In this session we are going to see how to harness the power of Azure Computer Vision for image classification, object and face detection and more. No Machine Learning expertise required!
Create personalized experiences using the Azure Personlizer service
In this session, we are going to see how we can enable our applications to display personalized content to our users, that improves over time based on their behaviour, by using the Azure Personalizer service.
Azure AI on-premises by using Docker
Azure Cognitive Services provide support for Docker containers to let you use the Azure APIs on-premises. In this session, we are going to see which services support containerization, the benefits a container like Docker provides and how to export, configure and install your app for use in a container.
Developing Solutions with Azure Machine learning studio
In this session, we are going to see how to use the designer and the Azure ML SDKs and CLI to quickly prep data, train and deploy machine learning models. We will see how several Azure Machine Learning features work like multiple framework support and advanced ML capabilities like automated machine learning and pipeline support.
Georgia Kalyva
Microsoft Technical Trainer @ Microsoft, MBA
Athens, Greece
Links
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