Build and Operate Machine Learning Solutions with Azure
Learn to create enterprise-ready ML solutions with Azure Machine Learning SDK, from data handling to model deployment and monitoring.
Learn to create enterprise-ready ML solutions with Azure Machine Learning SDK, from data handling to model deployment and monitoring.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Microsoft Azure Data Scientist Associate (DP-100) Exam Prep Professional Certificate program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
3.9
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What you'll learn
Create and manage Azure Machine Learning workspaces using the Python SDK
Work with datastores and datasets for efficient data handling in the cloud
Train models using various compute targets and register them in your workspace
Orchestrate end-to-end ML workflows using pipelines with reusable components
Deploy models as real-time services and batch inference pipelines
Implement automated machine learning to discover optimal models automatically
Skills you'll gain
This course includes:
31 Hours PreRecorded video
12 quizzes, 23 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
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There are 6 modules in this course
This comprehensive course teaches data scientists how to leverage Azure Machine Learning to create enterprise-ready machine learning solutions in the cloud. The curriculum covers the entire ML lifecycle, from workspace setup and data preparation to model training, deployment, and monitoring. Students learn to use the Azure Machine Learning Python SDK to create and manage datastores, datasets, compute targets, and pipelines for efficient model training and deployment. Advanced topics include automated machine learning, hyperparameter tuning, differential privacy, model explanation, fairness assessment, and drift detection. The course prepares students for the DP-100 certification exam with hands-on exercises and real-world scenarios that demonstrate how to build and operate production-ready machine learning solutions on the Microsoft Azure platform.
Use the Azure Machine Learning SDK to train a model
Module 1 · 5 Hours to complete
Work with Data and Compute in Azure Machine Learning
Module 2 · 5 Hours to complete
Orchestrate pipelines and deploy real-time machine learning services with Azure Machine Learning
Module 3 · 5 Hours to complete
Deploy batch inference pipelines and tune hyperparameters with Azure Machine Learning
Module 4 · 4 Hours to complete
Select models and protect sensitive data
Module 5 · 4 Hours to complete
Monitor machine learning deployments
Module 6 · 5 Hours to complete
Fee Structure
Instructor
Empowering Individuals and Organizations Through Technology
Microsoft's mission is "to empower every person and every organization on the planet to achieve more," reflecting its commitment to leveraging technology for global empowerment. The company aims to drive digital transformation through an integrated cloud approach, creating a robust platform that enhances productivity and accessibility for users worldwide. Its vision statement emphasizes the goal of democratizing artificial intelligence, ensuring that AI technologies are accessible and beneficial for everyone. This focus on empowerment and inclusivity underpins Microsoft's strategies and product development, positioning it as a leader in innovation within the technology sector. The company has consistently pursued excellence by fostering a culture of innovation, diversity, and corporate social responsibility, ultimately aiming to improve quality of life and facilitate progress across various fields, including education, healthcare, and business.
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3.9 course rating
60 ratings
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