Kubeflow on Azure Training

Kubeflow on Azure

Master the art of deploying Machine Learning workloads on Azure using Kubeflow.

This instructor-led course offers hands-on experience, enabling you to leverage Kubernetes and TensorFlow for streamlined ML model deployment and management.

What will you learn?

This course is designed for engineers aiming to escalate Machine Learning workloads on Azure. Conducted by seasoned professionals, the training will take you through the intricacies of Kubeflow, Kubernetes, and TensorFlow.

What You Will Achieve:

Grasp the nuances of Kubeflow and how it interfaces with Kubernetes.
Acquire the skills to set up and manage Azure Kubernetes Service (AKS).
Learn to create robust Kubernetes pipelines for ML model automation.
Gain experience in multi-GPU and parallel machine training with TensorFlow.
Extend ML capabilities with Azure's managed services.

Requirements:

Familiarity with machine learning and cloud computing concepts.
Basic understanding of containers (Docker) and orchestration (Kubernetes).
Working knowledge of command-line interfaces.
Python programming experience is beneficial but not mandatory.

Course Outline*:

*We know each team has their own needs and specifications. That is why we can modify the training outline per need.

1. Introduction:

Comparison: Kubeflow on Azure, On-premise, and other public clouds
Architectural Overview of Kubeflow

2. Setting the Stage:

Activating an Azure Account
Launching GPU-Enabled Virtual Machines
User Roles and Permissions: A Primer

3. Building the Environment:

Preparing the Build Environment
Introduction to TensorFlow Models and Datasets
Packaging Code: Dockerization

4. Deployment Infrastructure:

Introduction to Azure Kubernetes Service (AKS)
Kubernetes Cluster Initialization with AKS

5. Data Management:

Data Staging: Training and Validation
Configuring Kubeflow Pipelines

6. Training and Monitoring:

Launching a Training Job
Real-Time Monitoring of Training Jobs

7. Post-Deployment:

Cleaning Up Resources
Troubleshooting Tips

8. Summary and Conclusion:

Key Takeaways
Next Steps in Leveraging Azure for ML Workloads

Hands-on learning with expert instructors at your location for organizations.

Level:

Intermediate

Duration:

Hours (days:

)

Training customized to your needs

Immersive hands-on experience in a dedicated setting

*Price can range depending on number of participants, change of outline, location etc.

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Master new skills guided by experienced instructors from anywhere.

Level:

Intermediate

Duration:

Hours (days:

)

Training customized to your needs

Reduced training costs

*Price can range depending on number of participants, change of outline, location etc.

Get Quote

You can participate in a Public Course with people from other organisations.

/per trainee

Thanks for the numbers, they could be going to your emails. But they're going to mine... Thanks ;D

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Level:

Intermediate

Duration:

Hours (days:

)

Fits ideally for individuals and small groups

Networking opportunities with fellow participants.

*Price can range depending on number of participants, change of outline, location etc.

Get Quote

Kubeflow on Azure

What will you learn?

Requirements:

Course Outline*:

1. Introduction:

2. Setting the Stage:

3. Building the Environment:

4. Deployment Infrastructure:

5. Data Management:

6. Training and Monitoring:

7. Post-Deployment:

8. Summary and Conclusion:

Get Quote for:

Kubeflow on Azure

Start your training experience here!