Overview

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

Syllabus

Get started with Microsoft Data Analytics

This course provides a comprehensive understanding of data science and machine learning, focusing on essential concepts and their applications. It emphasizes the fundamental principles of data analysis, statistical modeling, and machine learning techniques, fostering a strong foundation for practical implementation. Participants will gain valuable insights into different types of machine learning, real-world use cases, and best practices for selecting appropriate models. The course also covers key ML terminology, data preprocessing, and the statistical foundations necessary for building robust solutions, preparing learners for both theoretical evaluation and hands-on projects.

Prepare a machine learning solution

This course provides an in-depth understanding of managing and utilizing datasets within Azure ML workflows using Azure Data Factory and Synapse Analytics. It emphasizes the principles of configuring and managing Azure Machine Learning environments through the CLI and SDK (v2), ensuring seamless integration and automation. Participants will explore techniques for sharing assets across workspaces, optimizing scalability with registries, and designing efficient ML workflows. Additionally, the course delves into monitoring, retraining, and scaling ML models using Apache Spark and MLOps practices, reinforcing best practices for lifecycle management in production environments.

Design a Machine Learning Solution

This course provides a deep dive into identifying appropriate data sources, formats, and ingestion strategies for machine learning projects in Azure, ensuring efficient data handling. It emphasizes the principles of selecting the right services and compute options for model training, optimizing performance and scalability. Participants will gain expertise in differentiating between real-time and batch deployment strategies based on consumption needs, enabling informed architectural decisions. Additionally, the course explores MLOps best practices, guiding learners through the design and implementation of scalable workflows and effective Azure ML environment organization, ensuring seamless integration and lifecycle management.