Course Outline
Introduction
- Kubeflow on AWS versus on-premises versus other public cloud providers
Overview of Kubeflow Features and Architecture
Activating an AWS Account
Preparing and Launching GPU-enabled AWS Instances
Setting up User Roles and Permissions
Preparing the Build Environment
Selecting a TensorFlow Model and Dataset
Packaging Code and Frameworks into a Docker Image
Setting up a Kubernetes Cluster Using EKS
Staging the Training and Validation Data
Configuring Kubeflow Pipelines
Launching a Training Job using Kubeflow in EKS
Visualising the Training Job in Runtime
Cleaning up After the Job Completes
Troubleshooting
Summary and Conclusion
Requirements
- A solid understanding of machine learning concepts.
- Knowledge of cloud computing principles.
- A general grasp of containers (Docker) and orchestration (Kubernetes).
- Some Python programming experience is beneficial.
- Experience working with a command line interface.
Audience
- Data science engineers.
- DevOps engineers interested in machine learning model deployment.
- Infrastructure engineers interested in machine learning model deployment.
- Software engineers wishing to integrate and deploy machine learning features within their applications.
Testimonials (3)
I've find out new interesting things about Lambda and Serverless
Oleg Buldumac - PUBLIC COURSE
Course - AWS Lambda for Developers
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.
