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)
he was patience and understood that we fall behind
Albertina - REGNOLOGY ROMANIA S.R.L.
Course - Deploying Kubernetes Applications with Helm
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.
