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Course Outline
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
Requirements
- An understanding of the software development cycle
- Experience building or working with Machine Learning models
- Familiarity with Python programming
Audience
- ML engineers
- DevOps engineers
- Data engineers
- Infrastructure engineers
- Software developers
35 Hours
Testimonials (1)
working with DevOps Toolchain
