Overview

This course is designed to provide a comprehensive foundation in Azure Machine Learning, equipping learners with the skills to deploy, manage, and optimize ML models efficiently. Participants will begin by exploring model deployment and consumption in Azure ML, understanding how to operationalize machine learning solutions in production environments. The course progresses to managing and evaluating models, covering key concepts such as performance monitoring, retraining strategies, and best practices for ensuring model accuracy. Learners will gain expertise in Azure AutoML workflows, from data preparation to model selection and evaluation, ensuring automated yet effective ML development. Additionally, the course covers key aspects of MLOps, enabling seamless integration with Azure services for scalable and secure machine learning operations. This course is structured into multiple modules, each featuring lessons and video lectures that provide theoretical insights and hands-on practice. Participants will engage with approximately 3:00–4:00 hours of instructional content, ensuring both conceptual understanding and practical application. To reinforce learning, graded and ungraded assignments are included within each module to test the ability of learners in real-world scenarios. Module 1: Azure AI Foundry: End-to-End Model Development & Optimization Module 2: Optimize model training with Azure Machine Learning By end of this course, you will be able to learn Understand the concepts of Azure AI Foundry, including its role in model optimization, fine-tuning, and retrieval-augmented generation (RAG) strategies. Learn how to explore and manage the Model Catalog and Collections within Azure AI Foundry and ML, and use compute resources effectively. Gain practical experience testing and manually evaluating prompts in the Azure AI Foundry portal playground, including tracking prompt variants. Discover how to create and configure search indexes in the Azure portal, using Azure AI Search for enhanced data retrieval and model deployment.

Syllabus

Azure AI Foundry: End-to-End Model Development & Optimization

This module provides a comprehensive understanding of Azure AI Foundry and its capabilities, equipping learners with the skills to leverage AI models for advanced applications. Participants will explore key concepts such as Retrieval Augmented Generation (RAG) for enhancing AI-driven responses, fine-tuning strategies for optimizing model performance, and best practices for deploying AI models in production environments. The module covers the Azure AI Foundry model catalog, compute considerations, and how to test and refine language models using the interactive playground. Learners will gain expertise in manually evaluating prompts, defining and tracking prompt variants, and utilizing Azure AI Search to create efficient search indexes. By the end of this module, participants will be prepared to work with Azure AI Foundry and ML tools, ensuring scalable and high-performing AI solutions for various enterprise applications.

Optimize model training with Azure Machine Learning

This module provides a comprehensive understanding of preparing machine learning workflows for production using Azure Machine Learning, equipping learners with the skills needed for scalable and efficient deployment. Participants will explore best practices for transitioning from notebooks to scripts, executing command jobs with parameters, and integrating MLflow for model tracking and evaluation. The module covers pipeline creation, custom components, and prebuilt workflows—including an Automobile Price Prediction pipeline—to automate and optimize ML processes. Learners will gain expertise in working with metrics, hyperparameters, and data transformation techniques, ensuring model performance and reliability. Additionally, the module emphasizes key aspects of production readiness, such as managing resources, tracking ML models, and refining training workflows for real-world applications. By the end of this module, participants will be equipped with practical knowledge to implement and manage robust ML pipelines within Azure Machine Learning effectively