Kubeflow Training and Certification in Astana, Kazakhstan

This training on Kubeflow provides participants with a comprehensive understanding of deploying, managing, and scaling machine learning workflows on Kubernetes. The course covers Kubeflow's architecture, components, and practical usage to simplify ML model lifecycle management. With hands-on sessions, participants will learn to deploy Kubeflow pipelines, manage distributed training, and monitor models in production. This training is ideal for ML engineers, DevOps professionals, and data scientists aiming to streamline their ML workflows.

Introduction to Kubeflow and Environment Setup

• Session 1: Introduction to Kubeflow

  • Overview of MLOps and Kubeflow.
  • Kubeflow architecture and core components.
  • Benefits of Kubeflow for ML workflows.

• Session 2: Setting up Kubeflow

  • Installing and deploying Kubeflow on Kubernetes.
  • Exploring the Kubeflow dashboard and components.

• Session 3: Kubeflow Pipelines Basics

  • Overview of Kubeflow Pipelines.
  • Creating and running simple pipelines.

Building and Managing ML Workflows

• Session 4: Advanced Kubeflow Pipelines

  • Designing complex pipelines with multiple steps.
  • Parameterizing pipelines for scalability.

• Session 5: Distributed Training with Kubeflow

  • Using Kubeflow components for distributed training (TFJob, PyTorchJob).
  • Best practices for managing distributed ML workloads.

• Session 6: Hyperparameter Tuning

  • Introduction to Katib for hyperparameter optimization.
  • Setting up and running tuning experiments.

Model Deployment and Monitoring

• Session 7: Serving Models with Kubeflow

  • Overview of KFServing for model serving.
  • Deploying and serving ML models in production.

• Session 8: Monitoring and Governance

  • Tools for monitoring ML models (e.g., Prometheus, Grafana).
  • Setting up alerts for model drift and performance issues.

• Session 9: Best Practices and Warp-up

  • Best practices for managing and scaling ML workflows with Kubeflow.
  • Participant queries and feedback.

This training on Kubeflow equips participants with the knowledge and practical skills to build, deploy, and manage scalable machine learning workflows. The hands-on sessions ensure that participants are industry-ready to implement MLOps workflows with Kubeflow in production environments.