Azure AI Training in Norway

Azure Machine Learning (ML) and Azure OpenAI are cloud services that empower teams to develop, assess, and deploy machine learning and generative AI capabilities into production applications.

This instructor-led, live training (available online or onsite) is designed for intermediate-level data and application professionals who aim to use Azure ML and Azure OpenAI to create, deploy, and manage end-to-end AI solutions.

By the end of this training, participants will be able to:

• Navigate Azure Machine Learning Studio and implement comprehensive ML workflows from start to finish.
• Prepare data, train, test, and evaluate models using appropriate validation metrics.
• Register, package, and deploy models reliably to managed endpoints.
• Integrate Azure OpenAI models and Azure AI Search into applications following best practices.

Format of the Course

• Interactive lectures and discussions.
• Plenty of exercises and hands-on practice.
• Practical implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in Norway (online or onsite) is aimed at beginner-level / intermediate-level / advanced-level developers and technical professionals who wish to use Microsoft Azure to build, test, and deploy AI agents for business applications.

By the end of this training, participants will be able to: understand AI agent architecture on Azure, create and configure a working agent, connect agents to business knowledge sources, evaluate and prepare agents for deployment.

This instructor-led, live training in Norway (online or onsite) is aimed at intermediate-level developers and technical professionals who wish to use Azure OpenAI to build secure, useful, and scalable enterprise AI solutions.

By the end of this training, participants will be able to: explain Azure OpenAI capabilities, create effective prompts, connect AI applications to enterprise data, and apply security and responsible AI practices.