Data Science and Machine Learning Engineering on Microsoft Azure

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.

This course is designed to provide a comprehensive foundation in Azure Machine Learning, equipping learners with essential skills for managing ML workflows within the Azure ML workspace. Participants will begin by understanding core workspace fundamentals, including environment setup, resource management, and key components for ML experimentation. The course progresses to advanced concepts such as optimizing compute resources, managing datasets effectively, and configuring high-performance ML pipelines. Learners will gain expertise in scaling ML workloads, fine-tuning data storage strategies, and applying best practices for secure and efficient model deployment. Additionally, the course covers advanced data and compute management techniques to enhance ML operations (MLOps) and ensure seamless integration with Azure services. 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: Experiment with Azure Machine Learning Module 2: Deploying, Consuming, Managing, and Evaluating Models with Azure Machine Learning By the end of this course, a learner will be able to Explore the process of registering, logging, and deploying MLflow models Understand and implement Responsible AI practices Understand the fundamentals of AutoML in Azure Learn about different machine learning algorithms and tasks Master how to interpret AutoML job results, ensuring success and optimizing model performance.

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