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Course Outline
Introduction
- Introduction to Kubernetes
- Overview of Kubeflow Features and Architecture
- Kubeflow on AWS vs on-premise vs on other public cloud providers
Setting up a Cluster using AWS EKS
Setting up an On-Premise Cluster using Microk8s
Deploying Kubernetes using a GitOps Approach
Data Storage Approaches
Creating a Kubeflow Pipeline
Triggering a Pipeline
Defining Output Artifacts
Storing Metadata for Datasets and Models
Hyperparameter Tuning with TensorFlow
Visualizing and Analyzing the Results
Multi-GPU Training
Creating an Inference Server for Deploying ML Models
Working with JupyterHub
Networking and Load Balancing
Auto Scaling a Kubernetes Cluster
Troubleshooting
Summary and Conclusion
Requirements
- Familiarity with Python syntax
- Experience with Tensorflow, PyTorch, or other machine learning framework
- An AWS account with necessary resources
Audience
- Developers
- Data scientists
35 Hours
Delivery Options
Private Group Training
Our identity is rooted in delivering exactly what our clients need.
- Pre-course call with your trainer
- Customisation of the learning experience to achieve your goals -
- Bespoke outlines
- Practical hands-on exercises containing data / scenarios recognisable to the learners
- Training scheduled on a date of your choice
- Delivered online, onsite/classroom or hybrid by experts sharing real world experience
Private Group Prices RRP from £9500 online delivery, based on a group of 2 delegates, £3000 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.
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Public Training
Please see our public courses
Testimonials (1)
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.
