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Course Outline
Introduction
- Kubeflow on IKS vs on-premise vs on other public cloud providers
Overview of Kubeflow Features on IBM Cloud
- IKS
- IBM Cloud Object Storage
Overview of Environment Setup
- Preparing virtual machines
- Setting up a Kubernetes cluster
Setting up Kubeflow on IBM Cloud
- Installing Kubeflow through IKS
Coding the Model
- Choosing an ML algorithm
- Implementing a TensorFlow CNN model
Reading the Data
- Accessing the MNIST dataset
Pipelines on IBM Cloud
- Setting up an end-to-end Kubeflow pipeline
- Customizing Kubeflow Pipelines
Running an ML Training Job
- Training an MNIST model
Deploying the Model
- Running TensorFlow Serving on IKS
Integrating the Model into a Web Application
- Creating a sample application
- Sending prediction requests
Administering Kubeflow
- Monitoring with Tensorboard
- Managing logs
Securing a Kubeflow Cluster
- Setting up authentication and authorization
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.
