Applied AI on Microsoft Azure
Training Course

This course covers the basics of machine learning, cognitive services, and how to build AI solutions on Azure. It includes hands-on exercises and projects to apply AI concepts in real-world scenarios.
Oleksandr Krakovetskyi
CEO & Co-founder at DevRain
Target audience
Developers, content creators, customer service professionals, and anyone keen to explore and leverage the capabilities of ChatGPT in various applications.
Skills you'll gain
ChatGPT, Large Language Models, Prompt Engineering
Course modules
Module 1. Introduction to AI and ML
Dive into the fascinating world of artificial intelligence and machine learning, understanding their foundational concepts and significance. This module sheds light on the myriad applications and benefits of AI, especially in business settings. Moreover, participants will get an overview of Microsoft's AI platform, which plays a pivotal role in enabling applied AI solutions in the modern digital landscape.
Module 2. Azure Cognitive Services
Azure Cognitive Services offers a suite of powerful tools that cater to various AI needs. This module introduces these services, encompassing areas from natural language understanding and translation to image and speech recognition. Participants will also discover decision services that enhance recommendation and personalization, and they'll see how OpenAI Service can be harnessed for even more advanced language tasks.
Module 3. Applied AI Services
Unlock the potential of AI services designed for practical applications. This module guides participants through tools like Form Recognizer, which extracts structured data from documents, and the Microsoft Bot Framework for conversational AI. Additionally, explore the wonders of Azure Cognitive Search, understand how Metrics Advisor aids in anomaly detection, and discover the capabilities of Semantic Kernel for AI-powered search.
Module 4. Azure Machine Learning
Harness the potential of Azure's robust machine learning platform. Participants will familiarize themselves with Azure Machine Learning Notebooks, which facilitate data exploration and model development. The module also touches upon the Azure Machine Learning Designer, a tool that allows for visual building of ML pipelines. Plus, delve into the realm of AutoML, which automates the selection and tuning of machine learning models.
Module 5. AI Builder in Power Automate
AI Builder in Power Automate revolutionizes business workflows. This module delves into the AI Builder component, exploring its capabilities ranging from document processing and object detection to predictive functionalities. Participants will learn how to seamlessly integrate AI Builder into business processes, enhancing automation and informed decision-making.
Module 6. ML.NET
ML.NET offers .NET developers a gateway into the world of machine learning. This module introduces participants to this powerful Microsoft framework, elucidating its foundational concepts and myriad use cases. Explore how ML.NET can be harnessed to craft custom machine learning models tailored for specific business applications, opening doors to innovative solutions.
Module 7. Semantic Kernel
Semantic Kernel (SK) stands at the intersection of AI and Large Language Models (LLMs), offering an extensible programming model. This module introduces participants to SK, emphasizing its role in integrating AI LLMs. Dive into the functionalities of SK, from natural language semantic functions to code native functions, and understand the advantages of embedding-based memory.
What our customers say:
Oleksandr's ability to cater to a wide audience, transcending geographical boundaries, speaks volumes about his proficiency in delivering content that resonates with individuals from different backgrounds and experiences.
Grigoris Damasiotis
Grigoris Damasiotis
Head of Data Science and Business Transformation CSE MCO at Sanofi