Course Outline
Introduction
- Machine Learning models versus conventional software applications
Overview of the DevOps Lifecycle
Overview of the Machine Learning Lifecycle
Implementing ML as Code with Data Integration
Core Components of an ML System
Case Study: Sales Forecasting Application
Data Acquisition
Data Validation Procedures
Data Transformation Processes
Transitioning from Data Pipelines to ML Pipelines for government operations
Constructing the Data Model
Model Training Procedures
Model Validation Standards
Reproducibility of Model Training
Model Deployment Strategies
Production Deployment of Trained Models
Testing ML Systems
Continuous Delivery Orchestration
Model Monitoring Protocols
Data Versioning Controls
Adapting, Scaling, and Maintaining an MLOps Platform for government use cases
Troubleshooting Methodologies
Summary and Conclusion
Requirements
- Proficiency in software development lifecycle methodologies
- Demonstrated expertise in developing or deploying Machine Learning models
- Competency in Python programming languages
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
