Course Outline
Introduction
- Comparing Machine Learning models with traditional software
DevOps Workflow Overview
Machine Learning Workflow Overview
ML as Code Plus Data
Core Components of an ML System
Case Study: Sales Forecasting Application
Data Accessing Methods
Data Validation Techniques
Data Transformation Processes
Transitioning from Data Pipeline to ML Pipeline
Constructing the Data Model
Model Training Procedures
Model Validation Strategies
Ensuring Reproducibility in Model Training
Model Deployment Strategies
Serving Trained Models in Production
Testing the ML System
Continuous Delivery Orchestration
Model Monitoring Practices
Implementing Data Versioning
Adapting, Scaling, and Maintaining the MLOps Platform
Troubleshooting Common Issues
Summary and Conclusion
Requirements
- A solid understanding of the software development lifecycle
- Experience in building 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
