As a Microsoft Azure AI engineer, you build, manage, and deploy AI solutions that leverage Azure AI.

Your responsibilities include participating in all phases of AI solutions development, including:

• Requirements definition and design
• Development
• Deployment
• Integration
• Maintenance
• Performance tuning
• Monitoring

You work with solution architects to translate their vision. You also work with data scientists, data engineers, Internet of Things (IoT) specialists, infrastructure administrators, and other software developers to:

• Build complete and secure end-to-end AI solutions.
• Integrate AI capabilities in other applications and solutions.

As an Azure AI engineer, you have experience developing solutions that use languages such as:

• Python
• C#

Module 1: Get started with Azure AI Services

Module 2: Develop generative AI apps in Azure

Module 3: Develop Generative AI solutions with Azure OpenAI in Foundry Models

Module 4: Develop AI agents on Azure

Module 5: Develop natural language solutions in Azure

Module 6: Develop computer vision solutions in Azure

Module 7: Implement knowledge mining with Azure AI Search

By the end of this course, you should be able to use Representational State Transfer (REST) APIs and SDKs to build secure image processing, video processing, natural language processing, knowledge mining, and generative AI solutions on Azure.

You should:

• Understand the components that make up the Azure AI portfolio and the available data storage options.
• Be able to apply responsible AI principle.

This course is ideal for software engineers and developers who are interested in building, managing, and deploying AI solutions on Azure. Attendees should have experience with C# or Python and be familiar with using REST-based APIs for AI applications.

This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.

Module 1: Explore and configure the Azure Machine Learning workspace

Module 2: Experiment with Azure Machine Learning

Module 3: Optimise model training with Azure Machine Learning

Module 4: Manage and review models in Azure Machine Learning

Module 5: Deploy and consume models with Azure Machine Learning

Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning.

This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow.

Successful Azure Data Scientists start this role with a fundamental knowledge of cloud computing concepts, and experience in general data science and machine learning tools and techniques.

Specifically:

• Creating cloud resources in Microsoft Azure.
• Using Python to explore and visualise data.
• Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow