Designing and Preparing Machine Learning Solutions in Azure

Welcome to the Azure ML: Designing and Preparing Machine Learning Solutions This course is designed to provide a comprehensive foundation in data science and machine learning, equipping learners with essential knowledge of key ML principles, data management, and real-world applications. Participants will explore managing machine learning environments and data workflows in Azure, gaining hands-on expertise in Azure Data Factory, Synapse Analytics, and Azure ML SDK (v2) to streamline ML lifecycle operations. Additionally, the course covers designing end-to-end ML solutions and MLOps architectures, ensuring effective model deployment, monitoring, and retraining strategies using Apache Spark and scalable workflows. Learners will gain the ability to select optimal services and compute options, differentiate between real-time and batch model deployment, and organize Azure ML environments effectively. This course is divided into three modules, each containing structured lessons and video lectures to enhance understanding. Participants will engage with approximately 3:00–4:00 hours of video-based instruction, offering both theoretical insights and practical knowledge. To reinforce learning, graded and ungraded assignments are included within each module, allowing learners to assess their understanding and application of key concepts. Module 1: Get started with Microsoft Data Analytics Module 2: Prepare a machine learning solution Module 3: Design a Machine Learning Solution By the end of this course, you will be able to learn Understand the core concepts of data science, machine learning, and the role of a data scientist. Learn about different types of machine learning and their real-world applications. Explore key data aspects, common ML terminology, and statistical foundations essential for modeling. Gain insights into various machine learning models and how to select appropriate solutions. This Course is for Data Scientists, Data Analysts, ML Engineers, and ML Associates, those who were mainly working with the Microsoft Azure Cloud Platform

• Understand core concepts of data science, machine learning, and data science roles
• Manage machine learning environments and data workflows on Azure
• Use Azure Data Factory, Synapse Analytics, and Azure ML SDK for ML lifecycle
• Design end-to-end ML solutions and MLOps architectures
• Select optimal services and compute options for real-time applications

• Basic familiarity with data science and machine learning concepts
• Readiness to practice through applied exercises or case-based work