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コース概要
Introduction
- Kubeflow on Azure vs on-premise vs on other public cloud providers
Overview of Kubeflow Features and Architecture
Overview of the Deployment Process
Activating an Azure Account
Preparing and Launching GPU-enabled Virtual Machines
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 AKS
Staging the Training and Validation Data
Configuring Kubeflow Pipelines
Launching a Training Job.
Visualizing the Training Job in Runtime
Cleaning up After the Job Completes
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 interested 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 (5)
それはまさに私たちが求めていたものであり、参加した社内エンジニアのさまざまなプロフィールをカバーする非常にバランスのとれた量のコンテンツと演習でした。
Arturo Sanchez - INAIT SA
コース - Microsoft Azure Infrastructure and Deployment
Machine Translated
I've got to try out resources that I've never used before.
Daniel - INIT GmbH
コース - Architecting Microsoft Azure Solutions
The Exercises
Khaled Altawallbeh - Accenture Industrial SS
コース - Azure Machine Learning (AML)
とてもフレンドリーで親切
Aktar Hossain - Unit4
コース - Building Microservices with Microsoft Azure Service Fabric (ASF)
Machine Translated
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
