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 Operations
Components of an ML System for Government Use
Case Study: A Sales Forecasting Application for Government Agencies
Accessing Data for Government Systems
Validating Data in Government Applications
Data Transformation for Government Projects
Transition from Data Pipeline to ML Pipeline for Government
Building the Data Model for Government Use
Training the Model for Government Operations
Validating the Model in Government Applications
Reproducing Model Training for Government Systems
Deploying a Model for Government Use
Serving a Trained Model to Production for Government Agencies
Testing an ML System for Government Operations
Continuous Delivery Orchestration for Government
Monitoring the Model in Government Applications
Data Versioning for Government Systems
Adapting, Scaling, and Maintaining an MLOps Platform for Government
Troubleshooting for Government ML Systems
Summary and Conclusion for Government Use
Requirements
- A comprehensive understanding of the software development lifecycle for government projects
- Practical experience in building or working with Machine Learning models
- Proficiency in Python programming
Audience
- 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.
