Training Kubeflow – machine learning orchestration on Kubernetes

Intensive training focusing on using the Kubeflow platform to orchestrate machine learning processes in a Kubernetes environment. The program guides participants through the process of building, deploying and managing ML pipelines in a distributed environment, combining theory with intensive hands-on practice. Hands-on workshops make up 70% of the training time, where participants work on real use cases, building end-to-end ML solutions using DevOps and MLOps best practices. Each topic is discussed in the context of practical production challenges, including aspects of scalability, reliability and efficiency.

• MLOps engineers implementing solutions in production
• DevOps specialists working with ML systems
• Cloud solution architects for AI/ML
• Data scientists interested in the operational aspects of ML
• Platform engineers dealing with ML infrastructure
• ML systems implementation specialists
• Kubernetes cluster administrators
• Teams responsible for the continuous integration and implementation of ML

1. Kubeflow basics and architecture
• Kubeflow components and architecture
• Integration with Kubernetes
• Cluster resource management
• Setting up the development environment
2. Pipelines and orchestration
• ML pipeline design
• Kubeflow components and operators
• Dependency management
• Monitoring and debugging streams
3. Advanced implementations
• Scaling ML streams
• Model version management
• Performance optimization
• ML process automation
4. MLOps and DevOps practices
• Continuous integration for ML
• Experiment management
• Production monitoring
• Security and access control

Upon completion of the training, the participant will have a comprehensive knowledge of designing and implementing ML solutions in the Kubernetes environment using Kubeflow. He or she will gain practical skills in building scalable ML pipelines that can support complex learning and inference processes. Will develop competence in managing the entire lifecycle of ML models in a production environment. Will learn to efficiently utilize computational resources and optimize the performance of ML pipelines. Will be able to implement MLOps best practices, ensuring reliable and secure implementations. Will gain the ability to integrate ML solutions with existing DevOps infrastructure and CI/CD processes.

• Practical knowledge of Kubernetes and containerization
• Experience in the implementation of ML solutions
• Knowledge of the basics of DevOps and CI/CD
• Python programming skills

• Kubeflow Architecture
• ML pipelines
• Kubernetes for ML
• Model serving
• Experiment tracking
• Resource management
• Pipeline optimization
• Version control
• Model deployment
• Monitoring of ML systems
• Security practices
• Infrastructure automation