Course Outline
Introduction
- Machine Learning models contrasted with traditional software
DevOps Workflow Overview
Machine Learning Workflow Overview
ML as Code Combined with Data
Elements of an ML System
Case Study: Sales Forecasting Application
Data Access
Data Validation
Data Transformation
Transitioning from Data Pipelines to ML Pipelines
Constructing the Data Model
Model Training
Model Validation
Replicating Model Training
Model Deployment
Serving a Trained Model in Production
Testing an ML System
Continuous Delivery Orchestration
Model Monitoring
Data Version Control
Adapting, Scaling, and Maintaining an MLOps Platform
Troubleshooting
Summary and Conclusion
Requirements
- Basic understanding of the software development lifecycle
- Practical experience developing or utilizing Machine Learning models
- Proficiency in 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
