Course Outline
Introduction
- Comparison of Machine Learning Models to Traditional Software
Overview of the DevOps Workflow for Government
Overview of the Machine Learning Workflow
Machine Learning as Code Plus Data
Components of a Machine Learning System
Case Study: A Sales Forecasting Application for Government Use
Accessing Data in Government Environments
Validating Data for Government Systems
Data Transformation for Government Applications
Transition from Data Pipeline to Machine Learning Pipeline
Building the Data Model for Government Use
Training the Model for Government Applications
Validating the Model in a Government Context
Reproducing Model Training for Government Systems
Deploying a Model in Government Environments
Serving a Trained Model to Production for Government Use
Testing an Machine Learning System for Government Compliance
Continuous Delivery Orchestration for Government Workflows
Monitoring the Model for Government Operations
Data Versioning for Government Systems
Adapting, Scaling, and Maintaining an MLOps Platform for Government Use
Troubleshooting in a Government Context
Summary and Conclusion
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 (3)
There were many practical exercises supervised and assisted by the trainer
Aleksandra - Fundacja PTA
Course - Mastering Make: Advanced Workflow Automation and Optimization
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
Guillaume GAUTIER - OLEA MEDICAL
Course - MLflow
I enjoyed participating in the Kubeflow training, which was held remotely. This training allowed me to consolidate my knowledge for AWS services, K8s, all the devOps tools around Kubeflow which are the necessary bases to properly tackle the subject. I wanted to thank Malawski Marcin for his patience and professionalism for training and advice on best practices. Malawski approaches the subject from different angles, different deployment tools Ansible, EKS kubectl, Terraform. Now I am definitely convinced that I am going into the right field of application.
