Course Outline
Foundations of MLOps on Kubernetes
- Core concepts of MLOps
- Differences between MLOps and traditional DevOps
- Key challenges in ML lifecycle management
Containerizing ML Workloads
- Packaging models and training code
- Optimizing container images for ML tasks
- Managing dependencies and ensuring reproducibility
CI/CD for Machine Learning
- Structuring ML repositories for automation
- Integrating testing and validation steps
- Triggering pipelines for retraining and updates
GitOps for Model Deployment
- GitOps principles and workflows
- Utilizing Argo CD for model deployment
- Version control for models and configurations
Pipeline Orchestration on Kubernetes
- Building pipelines with Tekton
- Managing multi-step ML workflows
- Scheduling and resource management
Monitoring, Logging, and Rollback Strategies
- Tracking data drift and model performance
- Integrating alerting and observability
- Rollback and failover approaches
Automated Retraining and Continuous Improvement
- Designing feedback loops
- Automating scheduled retraining
- Integrating MLflow for tracking and experiment management
Advanced MLOps Architectures
- Multi-cluster and hybrid-cloud deployment models
- Scaling teams with shared infrastructure
- Security and compliance considerations
Summary and Next Steps
Requirements
- A solid understanding of Kubernetes fundamentals
- Practical experience with machine learning workflows
- Familiarity with Git-based development practices
Audience
- ML engineers
- DevOps engineers
- ML platform teams
Testimonials (3)
The knowledge and the patience from the trainer to answer to our questions.
Calin Avram - 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.
