Course Outline
Foundations of MLOps on Kubernetes for Government
- Core concepts of MLOps for government applications
- Comparison of MLOps and traditional DevOps in the public sector
- Key challenges in managing the ML lifecycle within government workflows
Containerizing ML Workloads for Government
- Packaging models and training code to meet government standards
- Optimizing container images for efficient use in government environments
- Managing dependencies and ensuring reproducibility in government projects
CI/CD for Machine Learning in Government
- Structuring ML repositories to support automation in government processes
- Integrating testing and validation steps to ensure compliance with government regulations
- Triggering pipelines for retraining and updates to maintain model accuracy and relevance
GitOps for Model Deployment in Government
- Principles and workflows of GitOps tailored for government use
- Using Argo CD for secure and efficient model deployment in government systems
- Version control of models and configurations to ensure traceability and accountability
Pipeline Orchestration on Kubernetes for Government
- Building pipelines with Tekton to support government projects
- Managing multi-step ML workflows in a government context
- Scheduling and resource management to optimize government operations
Monitoring, Logging, and Rollback Strategies for Government
- Tracking data drift and model performance to ensure reliability in government applications
- Integrating alerting and observability tools to enhance transparency and accountability
- Implementing rollback and failover approaches to maintain system integrity
Automated Retraining and Continuous Improvement for Government
- Designing feedback loops to refine government models over time
- Automating scheduled retraining to keep government systems up-to-date
- Integrating MLflow for tracking and experiment management in government projects
Advanced MLOps Architectures for Government
- Multi-cluster and hybrid-cloud deployment models to support government scalability
- Scaling teams with shared infrastructure to enhance collaboration within government agencies
- Security and compliance considerations specific to government operations
Summary and Next Steps for Government
Requirements
- A comprehensive understanding of Kubernetes fundamentals for government applications
- Practical experience with machine learning workflows in a public sector environment
- Proficiency in Git-based development for government projects
Audience
- Machine Learning (ML) engineers for government agencies
- DevOps engineers supporting public sector initiatives
- ML platform teams within governmental organizations
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.
