Thank you for sending your enquiry! One of our team member will contact you shortly.
Thank you for sending your booking! One of our team member will contact you shortly.
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
Testimonials
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.
Guillaume Gautier - OLEA MEDICAL | Improved diagnosis for life™
Kubeflow Course
Adjusting to our needs
Sumitomo Mitsui Finance and Leasing Company, Limited
Kubeflow Course
Related Courses
Data Mining with Weka
14 hours
AdaBoost Python for Machine Learning
14 hours
Machine Learning with Random Forest
14 hours
DataRobot
7 hours
H2O AutoML
14 hours
AutoML with Auto-sklearn
14 hours
AutoML with Auto-Keras
14 hours
AutoML
14 hours
Google Cloud AutoML
7 hours
Advanced Analytics with RapidMiner
14 hours
Pattern Recognition
21 hours
Pattern Matching
14 hours