Course Outline
Introduction
- Comparison of Machine Learning Models to Traditional Software
Overview of the DevOps Workflow for Government
Overview of the Machine Learning Workflow for Government
ML as Code Plus Data for Government
Components of an ML System for Government
Case Study: A Sales Forecasting Application for Government
Accessing Data for Government
Validating Data for Government
Data Transformation for Government
Transition from Data Pipeline to ML Pipeline for Government
Building the Data Model for Government
Training the Model for Government
Validating the Model for Government
Reproducing Model Training for Government
Deploying a Model for Government
Serving a Trained Model to Production for Government
Testing an ML System for Government
Continuous Delivery Orchestration for Government
Monitoring the Model for Government
Data Versioning for Government
Adapting, Scaling, and Maintaining an MLOps Platform for Government
Troubleshooting for Government
Summary and Conclusion for Government
Requirements
- A comprehensive understanding of the software development lifecycle
- Practical experience in developing or working with Machine Learning models
- Proficiency in Python programming
Audience for government
- Machine Learning 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
