/aws-lambda-data-preprocessing

A lambda function to perform data preprocessing on new data placed into an AWS S3 Bucket.

Primary LanguagePython

AWS Lambda Data Preprocessing

Python 3.12 🚧 Bump version 🚀 Push Docker image to AWS ECR 🧹 Run linter Code style: black

A lambda to perform data pre-processing on new data put into an S3 bucket. An assumption has been made that new data uploaded will be of the same format e.g. same features, data schema etc. Actions performed not limited to removing missing data, imputing numerical values and/or categorical values etc.

This repository does not create the S3 Bucket, this is created via Terraform found here terraform-aws-machine-learning-pipeline. Data uploaded into these buckets can be found here ml-data-copy-to-aws-s3. Additionally, data preparation is specific to a specific set of data found within the GitHub repository.

Flowchart

The diagram below demonstrates what happens when the lambda is trigger, when a new .csv object has been uploaded to the S3 Bucket.

graph LR
  S0(Start)
  T1(Pull dataset from S3 Bucket)
  T2(Dataset transformed using Pandas)
  T3(Upload transformed data to output bucket)
  T4(Tag original dataset as processed)
  E0(End)

  S0-->T1
  T1-->T2
  T2-->T3
  T3-->T4
  T4-->E0
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Notice

As mentioned in the project description, the code provided here is to be used for a specific dataset which is predicting if potential customers will engage with offers provided to them. Using various information, such as demographic, past interactions, and environmental factors and only target a specific set of customers with an offer.

Data pre-processing if done incorrectly is one of the biggest risk in every machine learning project. Please ensure that enough time is taken and the data received is properly understood before attempting to automate the process.

Development

Dependencies

Usage

  1. Build the docker image locally:

    docker build --no-cache -t data-preprocessing:local .

  2. Run the docker image built:

    docker run --platform linux/amd64 -p 9000:8080 data-preprocessing:local

  3. Send an event to the lambda via curl:

    curl "http://localhost:9000/2015-03-31/functions/function/invocations" -d '{<EXPAND_BELOW_AND_REPLACE_WITH_JSON_BELOW>}'
    Example AWS S3 event received ```json { "Records": [ { "eventVersion": "2.1", "eventSource": "aws:s3", "awsRegion": "eu-west-2", "eventTime": "2023-12-01T21:48:58.339Z", "eventName": "ObjectCreated:Put", "userIdentity": { "principalId": "AWS:ABCDEFGHIJKLMNOPKQRST" }, "requestParameters": { "sourceIPAddress": "127.0.0.1" }, "responseElements": { "x-amz-request-id": "BY65CG6WZD6HBVX2", "x-amz-id-2": "c2La85nMEE2WBGPHBXDc5a8fd28kEpGt/QsP8n/xmbLv0ZAJeqsK/XmNcCCS+phWuVz8KP3/gn3Ql3/z7RPyC3n176rqpzvZ" }, "s3": { "s3SchemaVersion": "1.0", "configurationId": "huh", "bucket": { "name": "example-bucket-name", "ownerIdentity": { "principalId": "ABCDEFGHIJKLMN" }, "arn": "arn:aws:s3:::example-bucket-name" }, "object": { "key": "data/bank-additional-full.csv", "size": 515246, "eTag": "0e29c0d99c654bbe83c42097c97743ed", "sequencer": "00656A54CA3D69362D" } } } ] } ```

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 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.