MB-MuratBayraktar / tf-fraud-detection-using-machine-learning

Use Terraform to set up infrastructure described in AWS's example of fraud detection with SageMaker. https://docs.aws.amazon.com/solutions/latest/fraud-detection-using-machine-learning/architecture.html

Geek Repo:Geek Repo

Github PK Tool:Github PK Tool

Fraud Detection Using Machine Learning

Setup end to end demo architecture for predicting fraud events with Machine Learning using Amazon SageMaker and Terraform. This repo supports the original article posted on Medium.

Terraform version

Ensure your Terraform version is as follows (some modifications would be required if you run other Terraform versions):

$ terraform --version
Terraform v0.11.14
+ provider.archive v1.2.2
+ provider.aws v2.21.1
+ provider.template v2.1.2

To download Terraform, visit https://releases.hashicorp.com/terraform/

Setup steps

From terraform folder:

  1. Copy terraform_backend.tf.template to terraform_backend.tf and modify values accordingly. You need to manually create an S3 bucket or use an existing one to store the Terraform state file.
  2. Copy terraform.tfvars.template to terraform.tfvars and modify values accordingly. You don't need to create any buckets specified in here, they're to be created by terraform apply.
  3. Run the followings:
export AWS_PROFILE=<your desired profile>

terraform init
terraform validate
terraform plan -out=tfplan
terraform apply --auto-approve tfplan

Clean up

terraform plan -destroy -out=tfplan
terraform apply tfplan

Original source

https://github.com/awslabs/fraud-detection-using-machine-learning

Original CloudFormation script can be found at cloudformation folder (renamed from deployment).

License

This library is licensed under the Apache 2.0 License.

About

Use Terraform to set up infrastructure described in AWS's example of fraud detection with SageMaker. https://docs.aws.amazon.com/solutions/latest/fraud-detection-using-machine-learning/architecture.html

License:Apache License 2.0


Languages

Language:HCL 47.0%Language:Jupyter Notebook 33.0%Language:Python 11.7%Language:Shell 8.3%