Course Outline
Introduction
- Distinguishing Machine Learning models from conventional software architectures
Comprehensive Overview of the DevOps Workflow
Comprehensive Overview of the Machine Learning Lifecycle
Integrating Code and Data in ML Frameworks
Core Components of an ML System
Practical Example: A Sales Forecasting Solution
Data Acquisition Protocols
Data Integrity and Validation Procedures
Data Processing and Transformation
Transitioning from Data Pipelines to ML Pipelines
Constructing the Data Model
Model Training Processes
Model Validation and Accuracy Assessment
Ensuring Reproducibility in Model Training
Model Deployment Strategies
Serving Trained Models in Production Environments
Quality Assurance and Testing for ML Systems
Continuous Delivery Orchestration
Model Monitoring and Performance Tracking
Data Version Control and Management
Adapting, Scaling, and Maintaining an MLOps Platform
Troubleshooting Methodologies
Summary and Conclusion
Requirements
- Proficiency in the software development lifecycle
- Practical experience in building or interacting with Machine Learning models
- Competence 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
