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コース概要
Introduction
- Kubeflow on OpenShift vs public cloud managed services
Overview of Kubeflow on OpenShift
- Code Read Containers
- Storage options
Overview of Environment Setup
- Setting up a Kubernetes cluster
Setting up Kubeflow on OpenShift
- Installing Kubeflow
Coding the Model
- Choosing an ML algorithm
- Implementing a TensorFlow CNN model
Reading the Data
- Accessing a dataset
Kubeflow Pipelines on OpenShift
- Setting up an end-to-end Kubeflow pipeline
- Customizing Kubeflow Pipelines
Running an ML Training Job
- Training a model
Deploying the Model
- Running a trained model on OpenShift
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
要求
- 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 時間
お客様の声 (3)
参加者が操作するためのサンドボックスの形式として仮想デスクトップを利用できるのは素晴らしいことです。
Benedict - Questronix Corporation
コース - OpenShift 4 for Administrators
Machine Translated
たくさんの追加ツール
Adam - ENIGMA SOI SP. Z O.O.
Machine Translated
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.
