- 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
Summary and Conclusion
- 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.
- 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.
That the trainer was able to adapt to specific questions/situations
- Inforit BV
Calmness and self-mastery of the lecturer and huge knowledge, transmitted in a simple and simple way, supported by practical examples. One of the better trainings I've participated in.
Tomasz Czajka - Unit4 Polska sp. z o.o.
Learning about Kubernetes.
Felix Bautista - SGS GULF LIMITED ROHQ
I would say that the trainor really explain well. I like his strategy on teaching. He ensures that we really learn before proceeding to the next topic.