Bespoke course for ASML
This is a bespoke course for ASML
Introduction
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Introduction to Kubernetes
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Introduction to FLASK API
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Expose ML Model as an Endpoint
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Pytest and Tox Automation Testing Framework
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Dockerize ML
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End to End CI/CD pipeline using Gemfury, Docker, Git, CircleCI and Heroku
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Overview of Kubeflow Features and Architecture
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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