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Course Outline
Introduction
- Kubeflow on AWS vs on-premise vs on 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
Visualizing the Training Job in Runtime
Cleaning up After the Job Completes
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 integrate and deploy machine learning features with their application.
28 Hours
Testimonials (3)
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
Guillaume GAUTIER - OLEA MEDICAL
Course - MLflow
All good, nothing to improve
Ievgen Vinchyk - GE Medical Systems Polska Sp. Z O.O.
Course - AWS Lambda for Developers
IOT applications
