Software Development Microsoft Azure Machine Learning and Artificial Intelligence Soft Skills Microsoft Office365
Athens, Attica, Greece
Georgia Kalyva is a Microsoft AI MVP and a Microsoft Certified Trainer, with years of experience in software engineering and is currently working for ITT as a Web Applications Developer. She holds a bachelor’s degree in Computer Science and a master’s degree in Business Administration. She is passionate about AI and Azure and has represented Greece in global competitions in Technology and Entrepreneurship. She is a member of the Microsoft Learn Ambassadors team and has taken over the role of mentor in several Microsoft competitions and trainings.
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.
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!
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 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.
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.