MLOps2: Data Pipeline Automation with GCP
This course is part of MLOps with Google Cloud Platform.
This comprehensive MLOps course focuses on automating and optimizing machine learning pipelines using Google Cloud Platform. Students learn to implement automated monitoring systems, handle data and model drift, and establish robust CI/CD practices. The curriculum covers essential topics including pipeline automation, model stability monitoring, trigger implementation, and responsible AI practices. Through hands-on experience, participants develop skills to build and maintain reliable ML systems while addressing ethical considerations in deployment.
Instructors:
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What you'll learn
Set up automated monitoring for ML data pipelines
Implement model drift and data drift detection systems
Design effective training and inference pipelines
Apply CI/CD principles in ML operations
Ensure model stability through monitoring and alerts
Implement responsible AI practices in production
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, Exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 6 modules in this course
The course addresses the challenges of deploying machine learning models in production environments using Google Cloud Platform. Students learn to implement automated monitoring systems for data pipelines, manage model drift and feedback loops, and ensure model stability. The curriculum covers both technical aspects like CI/CD implementation and ethical considerations in machine learning deployments. Practical hands-on exercises help students master pipeline automation, trigger configuration, and responsible AI practices.
Training Versus Inference Pipelines
Module 1
Drift & Feedback Loops
Module 2
Triggers & Alarms
Module 3
Model Stability
Module 4
CI/CD
Module 5
Responsible AI
Module 6
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: MLOps with Google Cloud Platform
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
1 Course
Dynamic Analytics Leader with Military Background and a Passion for Data Solutions
Evan Wimpey is the Director of Analytics Strategy at Elder Research, where he combines his military experience with a strong background in data science to deliver innovative solutions for clients. Since joining Elder Research in 2019, he has focused on helping organizations leverage analytics to address complex challenges. Evan holds a Master of Science in Analytics from the Institute for Advanced Analytics and an MS in Economics from East Carolina University, along with a BS in Management from Georgia Tech. Known for his engaging presentation style, he excels at making technical analyses accessible to non-technical audiences. In addition to his analytics work, Evan is also a stand-up comedian, using humor to enhance the learning experience and foster engagement in data-driven decision-making. His diverse career includes roles in marketing, military operations, and financial management, reflecting his commitment to bridging the gap between data science and practical application.
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