MLOps with Azure: Model Deployment
Master ML model deployment in production using Azure Machine Learning, from pipeline creation to performance monitoring.
Master ML model deployment in production using Azure Machine Learning, from pipeline creation to performance monitoring.
This comprehensive course focuses on deploying AI and ML models in production environments using Microsoft Azure Machine Learning. Learn essential MLOps practices including data pipeline development, model versioning, performance monitoring, and automated deployment. The course addresses common deployment challenges and teaches best practices for successful implementation of machine learning projects in production.
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What you'll learn
Master the deployment of machine learning models in production environments
Build and maintain efficient data pipelines for ML operations
Implement model and data versioning best practices
Develop monitoring systems for model performance tracking
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, Exams
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
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There are 4 modules in this course
This course provides comprehensive training in deploying machine learning models using Microsoft Azure. Students learn to bridge the gap between data science and production engineering, covering crucial aspects of the ML pipeline lifecycle. The curriculum focuses on practical implementation, including model deployment, performance monitoring, data versioning, and artifact management. Special emphasis is placed on collaboration between data engineers and data scientists, ensuring successful model deployment and maintenance in production environments.
The Machine Learning Pipeline
Module 1
The Model in the Pipeline
Module 2
Monitoring Model Performance
Module 3
Training Artifacts and Model Store
Module 4
Fee Structure
Instructors
5 Courses
Prominent Educator and Author in Statistics and Data Science
Peter Bruce is the Chief Learning Officer at Elder Research and the Founder of the Institute for Statistics Education at Statistics.com, which specializes in online education in statistics and data analytics. He has co-authored several influential works, including Responsible Data Science (Wiley, 2021), Data Mining for Business Analytics (Wiley, 2006–2021), which has seen 13 editions and is used in over 600 universities worldwide, and Practical Statistics for Data Scientists (O'Reilly, 2nd ed. 2020). Additionally, he authored Introductory Statistics and Analytics: A Resampling Perspective (Wiley, 2015). With a background that includes degrees from Princeton and Harvard, as well as an MBA from the University of Maryland, Peter has leveraged his extensive knowledge to develop a comprehensive curriculum that addresses various aspects of statistics and analytics. His commitment to education is reflected in his role at the Institute, where he oversees course development and faculty recruitment while teaching courses on resampling methods
3 Courses
Pioneering Data Scientist and Founder of Elder Research
Dr. John Elder is the Chairman of the Board and Founder of Elder Research, Inc., recognized as one of the most experienced data science consulting teams in the United States. Since founding the company in 1995, he has led efforts to solve complex challenges for both commercial and government clients by extracting actionable insights from diverse data sources. Dr. Elder has co-authored several influential works, including Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications, Handbook of Statistical Analysis and Data Mining Applications, and Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions. His contributions to data mining tools and ensemble methods have significantly impacted the field, and he is a sought-after keynote speaker and chair of international conferences. With degrees in Electrical Engineering from Rice University and a PhD in Systems Engineering from the University of Virginia, Dr. Elder combines academic rigor with practical application, enhancing the capabilities of data science across various industries
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