A repository to host examples for notebooks, pipelines, batch scoring and deployment on Bedrock
This repository is home to the following types of examples:
A copy of the hcl template is also provided as bedrock.hcl.tmpl
.
This example covers the following concepts:
- Set up a Bedrock training pipeline, either on Google Cloud or AWS
- Monitor the training
- Deploy a model endpoint in HTTPS
- Query the endpoint
- Monitor the endpoint API metrics
This example covers the following concepts:
- Set up a Bedrock training pipeline
- Log training-time feature and inference distributions
- Log model explainability and fairness metrics
- Check model explainability and fairness from Bedrock web UI
- Deploy a model endpoint in HTTPS with logging feature and inference distributions
- Monitor the endpoint by simulating a query stream
This example covers the following concepts:
- Train a multiclass classification machine learning model
- Log metrics for multiclass classfication
- Log ROC, PR, and confusion matrices by micro-averaging on all classes
- Customise logging of feature distributions
- Log predictions for multiclass classification
- Log model explainabilty and fairness for multiclass
- Serving code for multiclass classification using bedrock express
This example covers the following concepts:
- Train a model with GPU on Bedrock
- Deploy a model endpoint
- Deploy a Streamlit app
This example covers the following concepts:
- Install conda packaages
- Activate a conda environment on bedrock
This example covers the following concepts:
- Write PySpark step in hcl file
This example covecrs the following concepts:
- Set up a Bedrock batch scoring pipeline on Google Cloud
- Save the output in Google BigQuery
This example covers the following concepts:
- Deploy a gRPC endpoint on Google Cloud