课程大纲： MLOps: CI/CD for Machine Learning培训
Introduction
Machine Learning models vs traditional software
Overview of the DevOps Workflow
Overview of the Machine Learning Workflow
ML as Code Plus Data
Components of an ML System
Case Study: A Sales Forecasting Application
Accessing Data
Validating Data
Data Transformation
From Data Pipeline to ML Pipeline
Building the Data Model
Training the Model
Validating the Model
Reproducing Model Training
Deploying a Model
Serving a Trained Model to Production
Testing an ML System
Continuous Delivery Orchestration
Monitoring the Model
Data Versioning
Adapting, Scaling and Maintaining an MLOps Platform
Troubleshooting
Summary and Conclusion
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