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January 28, 2025
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This course is part of Microsoft AI & ML Engineering Professional Certificate
Instructor: Microsoft
Included with
Recommended experience
Intermediate level
You should have completed the first three courses in the program, or have equivalent experience with the concepts taught in those courses.
Recommended experience
Intermediate level
You should have completed the first three courses in the program, or have equivalent experience with the concepts taught in those courses.
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January 2025
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This course provides hands-on experience with Microsoft Azure's AI and ML services. You will learn to set up, manage, and troubleshoot Azure-based AI & ML workflows. The course covers the entire ML lifecycle in Azure, from data preparation to model deployment and monitoring.
By the end of this course, you will be able to: 1. Configure and manage Azure resources for AI & ML projects. 2. Implement end-to-end ML pipelines using Azure services. 3. Deploy and monitor ML models in Azure production environments. 4. Troubleshoot common issues in Azure AI & ML workflows. To be successful in this course, you should have intermediate programming knowledge of Python, plus experience with AI & ML infrastructure, core AI & ML algorithms and techniques, and the design and implementation of intelligent troubleshooting agents. Familiarity with statistics is also recommended.
This module provides a comprehensive guide to setting up and managing Azure resources specifically tailored for AI and ML projects. As organizations increasingly leverage Azure's cloud infrastructure to build and deploy AI/ML solutions, understanding how to configure and manage these resources efficiently becomes critical. This module equips you with the skills to configure Azure resources, set up Azure Machine Learning workspaces, implement data storage solutions, and establish secure access controls. The module includes a blend of theoretical knowledge and practical exercises, featuring hands-on labs and real-world scenarios to reinforce learning objectives. You'll have the opportunity to apply your skills in a controlled environment, ensuring you gain practical experience in configuring and managing Azure resources for AI/ML projects.
9 videos13 readings7 assignments
This module delves into the intricacies of building and managing comprehensive data workflows and ML processes on Azure. The module covers the end-to-end process of ingesting data, preprocessing it, training ML models, and overseeing the training life cycle. Learners will gain hands-on experience with Azure services that streamline and enhance data and ML operations, ensuring effective management and monitoring of ML projects. You will engage in hands-on exercises to apply your knowledge in building and managing data ingestion pipelines, preprocessing data, training ML models, and monitoring ML processes. Through interactive sessions and guided practices, you'll develop the skills necessary to effectively manage end-to-end data and ML workflows in Azure.
8 videos7 readings6 assignments
This module focuses on the critical aspects of deploying, managing, and monitoring ML models within Azure production environments. This module provides a detailed exploration of best practices for model deployment, continuous integration and delivery (CI/CD), version control, and performance monitoring. You will learn to streamline the model life cycle from deployment to ongoing management, ensuring robust and reliable ML operations. Through interactive learning and guided practice, you will acquire the skills needed to effectively manage the life cycle of ML models in Azure production environments.
7 videos10 readings7 assignments
This module focuses on the essential skills needed to troubleshoot, diagnose, and optimize AI and ML pipelines in Azure. The module covers the identification and resolution of common issues in Azure AI/ML workflows, systematic troubleshooting methods, effective use of diagnostic tools, and the implementation of automated alerts and remediation strategies. You will learn how to maintain the smooth operation and performance of AI/ML pipelines, ensuring reliable and efficient deployments. Through interactive sessions and guided practices, you'll develop the skills necessary to effectively troubleshoot and optimize your Azure AI/ML environments.
10 videos9 readings7 assignments
This module provides a deep dive into practical strategies for addressing Azure issues, securing environments, and preparing for future software integrations. The module focuses on examining real-world use cases, understanding the ramifications of unsecured environments, and leveraging Azure documentation for continued learning. You will engage in ideation and discussion to anticipate potential issues and develop solutions for future integrations. Through collaborative learning and practical application, you'll develop a comprehensive approach to managing and securing Azure environments effectively.
6 videos8 readings4 assignments
Our goal at Microsoft is to empower every individual and organization on the planet to achieve more. In this next revolution of digital transformation, growth is being driven by technology. Our integrated cloud approach creates an unmatched platform for digital transformation. We address the real-world needs of customers by seamlessly integrating Microsoft 365, Dynamics 365, LinkedIn, GitHub, Microsoft Power Platform, and Azure to unlock business value for every organization—from large enterprises to family-run businesses. The backbone and foundation of this is Azure.
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To be successful in this course, you should have intermediate programming knowledge of Python, plus experience with AI & ML infrastructure, core AI & ML algorithms and techniques, and the design and implementation of intelligent troubleshooting agents. Familiarity with statistics is also recommended.
You will need a license to Microsoft Azure (or a free trial version) and appropriate hardware. Note: the free trial version of Azure is time limited and may expire before completion of the program.
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
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