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Course
- Data
Production Machine Learning Systems
This course covers how to implement the various flavors of production ML systems— static, dynamic, and continuous training; static and dynamic inference; and batch and online processing.
What you'll learn
This course covers how to implement the various flavors of production ML systems— static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. You delve into TensorFlow abstraction levels, the various options for doing distributed training, and how to write distributed training models with custom estimators. This is the second course of the Advanced Machine Learning on Google Cloud series. After completing this course, enroll in the Image Understanding with TensorFlow on Google Cloud course.
Table of contents
- Architecting ML systems | 2m 12s
- Data extraction, analysis, and preparation | 5m 8s
- Model training, evaluation, and validation | 2m 27s
- Trained model, prediction service, and performance monitoring | 2m 56s
- Training design decisions | 4m 45s
- Serving design decisions | 6m 3s
- Designing from scratch | 2m 38s
- Using Vertex AI | 8m 43s
- Lab introduction: Structured data prediction | 22s
- Pluralsight: Getting Started with GCP and Qwiklabs | 3m 48s
- Lab: Structured data prediction using Vertex AI Platform | 10s
- Readings: Architecting production ML systems | 10s
