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Course Outline
Introduction
Overview of Kubeflow Features and Components
- Containers, manifests, etc.
Overview of a Machine Learning Pipeline
- Training, testing, tuning, deploying, etc.
Deploying Kubeflow to a Kubernetes Cluster
- Preparing the execution environment (training cluster, production cluster, etc.)
- Downloading, installing and customizing.
Running a Machine Learning Pipeline on Kubernetes
- Building a TensorFlow pipeline.
- Building a PyTorch pipleline.
Visualizing the Results
- Exporting and visualizing pipeline metrics
Customizing the Execution Environment
- Customizing the stack for diverse infrastructures
- Upgrading a Kubeflow deployment
Running Kubeflow on Public Clouds
- AWS, Microsoft Azure, Google Cloud Platform
Managing Production Workflows
- Running with GitOps methodology
- Scheduling jobs
- Spawning Jupyter notebooks
Troubleshooting
Summary and Conclusion
Requirements
- Familiarity with Python syntax
- Experience with Tensorflow, PyTorch, or other machine learning framework
- A public cloud provider account (optional)
Audience
- Developers
- Data scientists
28 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 £7600 online delivery, based on a group of 2 delegates, £2400 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.
