Course Outline
Foundations of MLOps on Kubernetes for Government
- Core Concepts of MLOps for Government
- MLOps Compared to Traditional DevOps in the Public Sector
- Key Challenges in Managing the ML Lifecycle for Government Operations
Containerizing ML Workloads for Government
- Packaging Models and Training Code for Government Use
- Optimizing Container Images for Machine Learning in Public Sector Applications
- Managing Dependencies and Ensuring Reproducibility in Government Projects
CI/CD for Machine Learning in the Public Sector
- Structuring ML Repositories to Support Automation for Government
- Integrating Testing and Validation Steps into Government Workflows
- Triggering Pipelines for Retraining and Updates in Government Systems
GitOps for Model Deployment in the Public Sector
- GitOps Principles and Workflows for Government
- Using Argo CD for Model Deployment in Government Agencies
- Version Control of Models and Configurations for Government Operations
Pipeline Orchestration on Kubernetes for Government
- Building Pipelines with Tekton for Government Use
- Managing Multi-Step ML Workflows in Government Projects
- Scheduling and Resource Management for Government Systems
Monitoring, Logging, and Rollback Strategies for Government
- Tracking Data Drift and Model Performance for Government Operations
- Integrating Alerting and Observability in Government ML Systems
- Rollback and Failover Approaches for Government Applications
Automated Retraining and Continuous Improvement for Government
- Designing Feedback Loops for Government ML Projects
- Automating Scheduled Retraining in Government Systems
- Integrating MLflow for Tracking and Experiment Management in the Public Sector
Advanced MLOps Architectures for Government
- Multi-Cluster and Hybrid-Cloud Deployment Models for Government
- Scaling Teams with Shared Infrastructure in the Public Sector
- Security and Compliance Considerations for Government MLOps
Summary and Next Steps for Government
Requirements
- An understanding of Kubernetes fundamentals for government applications.
- Experience with machine learning workflows in a public sector environment.
- Knowledge of Git-based development practices for government projects.
Audience
- Machine Learning Engineers
- DevOps Engineers
- Machine Learning Platform Teams
Testimonials (3)
About the microservices and how to maintenance kubernetes
Yufri Isnaini Rochmat Maulana - Bank Indonesia
Course - Advanced Platform Engineering: Scaling with Microservices and Kubernetes
How trainer deliver knowledge so effectively
Vu Thoai Le - Reply Polska sp. z o. o.
Course - Certified Kubernetes Administrator (CKA) - exam preparation
The knowledge and the patience from the trainer to answer to our questions.
