Course Outline
Introduction
- Machine Learning models versus 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
Data Access
Data Validation
Data Transformation
Transitioning from Data Pipeline to ML Pipeline
Constructing the Data Model
Model Training
Model Validation
Reproducing Model Training
Model Deployment
Serving a Trained Model to Production
Testing an ML System
Continuous Delivery Orchestration
Model Monitoring
Data Versioning
Adapting, Scaling, and Maintaining an MLOps Platform
Troubleshooting
Summary and Conclusion
Requirements
- Understanding of the software development lifecycle
- Experience in building or working with Machine Learning models
- Familiarity with Python programming
Target Audience
- ML engineers
- DevOps engineers
- Data engineers
- Infrastructure engineers
- Software developers
Testimonials (2)
Craig was extremely involved in the training, always making sure we are paying attention, adapted the examples to our day-to-day activities and always provided an answer when asked, even if the information was not added in the presentation.
Ecaterina Ioana Nicoale - BOOKING HOLDINGS ROMANIA SRL
Course - DevOps Foundation®
High level of commitment and knowledge of the trainer
