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AutoPilot Workshop
How Amazon SageMaker Autopilot works
Getting Started
Load Autopilot Example
Run Autopilot Example
Workshop Clean-up
SageMaker Operators for Kubernetes Workshop
Getting Started
Overview
Prerequisites
Install Eksctl
Create Cluster
Install Kubeflow
Install SageMaker Operators
Dashboard
Dashboard
Notebook
Create a notebook
Data Preparation
Training and Inference
Training
Inference
Batch Inference
Hyper Parameter Optimization
Improving the TensorFlow Model
Network Improvements
Data Preparation
Training Script
Training and Inference
Next Steps
Next Steps
Cleanup
SageMaker Components for Kubeflow Pipelines Workshop
Getting Started
Overview
Prerequisites
Creating the Pipeline
S3 Bucket
SageMaker Job Role
Compile the Pipeline Definition
Register the Pipeline
Running the Pipeline
Run the Pipeline
Check Pipeline Status
Batch Transform Output
Real Time Inference
Deep Learning Pipeline
Create a Jupyter Notebook
Create and Run Pipeline
Next Steps
Resources
Use Existing Code
Use Existing Tensorflow Code
Use Existing Pytorch Code
Use Existing MxNet Code
Use Existing Scikit Learn Code
Use Existing R Code
Using SageMaker Spark
Advanced Topics
Reinforcement Learning
Graph Neural Networks
Automatically Stopping Training jobs
Generative AI
Iterative Model Pruning
Model specific real-time analysis
Visualization related
Sample SageMaker Use Cases
Structured data
Time Series
Computer Vision
Natural Language Processing
SageMaker Marketplace Examples on SageMaker
Specific SageMaker Features
Prepare Datasets
Build, Train and Deploy models
Use Advanced Training capabilities
Use Advanced Deployment capabilities
Optimize Costs
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Automatically Stopping Training jobs
Tensorflow stop training job based on rule
Stop notebook instance based on idle CPU cycles
Other lifecycle configuration scripts for SageMaker