https://github.com/kwame-mintah/aws-lambda-model-evaluation
A lambda to invoke inference requests to AWS SageMaker endpoints, using test data previous created. Helping with monitoring model performance.
https://github.com/kwame-mintah/aws-lambda-model-evaluation
aws aws-lambda model-evaluation python python312
Last synced: about 1 year ago
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A lambda to invoke inference requests to AWS SageMaker endpoints, using test data previous created. Helping with monitoring model performance.
- Host: GitHub
- URL: https://github.com/kwame-mintah/aws-lambda-model-evaluation
- Owner: kwame-mintah
- Created: 2024-03-25T21:24:23.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-08-30T13:17:04.000Z (over 1 year ago)
- Last Synced: 2025-02-01T11:43:48.287Z (about 1 year ago)
- Topics: aws, aws-lambda, model-evaluation, python, python312
- Language: Python
- Homepage:
- Size: 71.3 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
Awesome Lists containing this project
README
# AWS Lambda Model Evaluation
[](https://www.python.org/downloads/release/python-3121/)
[](https://github.com/kwame-mintah/aws-lambda-model-evaluation/actions/workflows/bump-repository-version.yml)
[](https://github.com/kwame-mintah/aws-lambda-model-evaluation/actions/workflows/push-docker-image-to-aws-ecr.yml)
[](https://github.com/kwame-mintah/aws-lambda-model-evaluation/actions/workflows/run-python-linter.yml)
A lambda to invoke an AWS SageMaker model endpoint with test data stored within an S3 Bucket. For analyzing a dataset to
[generate baseline constraints](https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_model_monitoring.html).
Allowing for monitoring machine learning (ML) model quality to determine if new model predictions are accurate or not,
additionally detect changes in properties.
This repository does not create the AWS Simple Queue Service, this is created via Terraform found here [terraform-aws-machine-learning-pipeline](https://github.com/kwame-mintah/terraform-aws-machine-learning-pipeline).
Additionally, data uploaded for ML purposes can be found here [ml-data-copy-to-aws-s3](https://github.com/kwame-mintah/ml-data-copy-to-aws-s3).
For more details on the entire flow and how this lambda is deployed, see [aws-automlops-serverless-deployment](https://github.com/kwame-mintah/aws-automlops-serverless-deployment).
# Flowchart
The [diagram below](https://mermaid.js.org/syntax/flowchart.html#flowcharts-basic-syntax) demonstrates what happens when
the lambda is trigger, when a new message is placed on the SQS.
```mermaid
graph LR
S0(Start)
T1(Check endpoint is available)
T2(Configure Predictor for endpoint)
T3(Pull test dataset from S3 Bucket)
T4(Use test data as payload to invoke endpoint)
T5(Save confusion matrix to S3 Bucket)
E0(End)
S0-->T1
T1-->T2
T2-->T3
T3-->T4
T4-->T5
T5-->E0
```
# Notice
An assumption has been made that a serverless endpoint has been created for inference. And not able to make of
[`DataCaptureConfig`](https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-data-capture-endpoint.html), this is
because the configuration is not supported for serverless endpoint(s) and will have to rely on CloudWatch logs as per
official [documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/serverless-endpoints-monitoring.html).
A workaround is to invoke the endpoint and create a confusion matrix with the predicated vs actuals, this is then uploaded
to another bucket as in Markdown format.
## Development
### Dependencies
- [Python](https://www.python.org/downloads/release/python-3121/)
- [Docker for Desktop](https://www.docker.com/products/docker-desktop/)
- [Amazon Web Services](https://aws.amazon.com/?nc2=h_lg)
## Usage
1. Build the docker image locally:
```shell
docker build --no-cache -t aws_lambda:local .
```
2. Run the docker image built:
```shell
docker run --platform linux/amd64 -p 9000:8080 aws_lambda:local
```
3. Send an event to the lambda via curl:
```shell
curl "http://localhost:9000/2015-03-31/functions/function/invocations" -d '{}'
```
Example AWS SQS event received
```json
{
"Records": [
{
"messageId": "059f36b4-87a3-44ab-83d2-661975830a7d",
"receiptHandle": "AQEBwJnKyrHigUMZj6rYigCgxlaS3SLy0a...",
"body": "{\"endpointName\": \"example\", \"testDataS3BucketName\": \"example-bucket\", \"testDataS3Key\" : \"test-data.csv\"}",
"attributes": {
"ApproximateReceiveCount": "1",
"SentTimestamp": "1545082649183",
"SenderId": "AIDAIENQZJOLO23YVJ4VO",
"ApproximateFirstReceiveTimestamp": "1545082649185"
},
"messageAttributes": {},
"md5OfBody": "098f6bcd4621d373cade4e832627b4f6",
"eventSource": "aws:sqs",
"eventSourceARN": "arn:aws:sqs:us-east-1:111122223333:my-queue",
"awsRegion": "us-east-1"
}
]
}
```
## GitHub Action (CI/CD)
The GitHub Action "🚀 Push Docker image to AWS ECR" will check out the repository and push a docker image to the chosen AWS ECR using
[configure-aws-credentials](https://github.com/aws-actions/configure-aws-credentials/tree/v4.0.1/) action. The following repository secrets need to be set:
| Secret | Description |
|--------------------|------------------------------|
| AWS_REGION | The AWS Region. |
| AWS_ACCOUNT_ID | The AWS account ID. |
| AWS_ECR_REPOSITORY | The AWS ECR repository name. |