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Course Outline
Introduction
- Kubeflow on GCK vs on-premise vs on other public cloud providers
Overview of Kubeflow Features on GCP
- Declarative management of resources
- GKE autoscaling for machine learning (ML) workloads
- Secure connections to Jupyter
- Persistent logs for debugging and troubleshooting
- GPUs and TPUs to accelerate workloads
Overview of Environment Setup
- Virtual machine preparation
- Kubernetes cluster setup
- Kubeflow installation
Deploying Kubeflow
- Deploying Kubeflow on GCP
- Deploying Kubeflow across on-premises and cloud environments
- Deploying Kubeflow on GKE
- Setting up a custom domain on GKE
Pipelines on GCP
- Setting up an end-to-end Kubeflow pipeline
- Customizing Kubeflow Pipelines
Securing a Kubeflow Cluster
- Setting up authentication and authorization
- Using VPC service controls and private GKE
Storing, Accessing, Managing Data
- Understanding shared filesystems and Network Attached Storage (NAS)
- Using managed file storage services in GCE
Running an ML Training Job
- Training an MNIST model
Administering Kubeflow
- Logging and monitoring
Troubleshooting
Summary and Conclusion
Requirements
- An understanding of machine learning concepts.
- Knowledge of cloud computing concepts.
- A general understanding of containers (Docker) and orchestration (Kubernetes).
- Some Python programming experience is helpful.
- Experience working with a command line.
Audience
- Data science engineers.
- DevOps engineers interesting in machine learning model deployment.
- Infrastructure engineers interesting in machine learning model deployment.
- Software engineers wishing to automate the integration and deployment of machine learning features with their application.
28 Hours
Testimonials (2)
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.
