Mlops with Aws Terraform
2 weeks ago
please see below for the detailed info about the MLOPS project requirement which IBM is looking for:
Usecase Name: Multi-account Secure MLOps Sagemaker Project with Gitlab CI/CD using Terraform
Usecase Description: This usecase involves the implementation of a robust MLOps workflow within an AWS environment, specifically using Amazon Sagemaker for machine learning tasks. The project ensures security through the utilization of multiple AWS accounts. Gitlab CI/CD is employed to automate the software delivery pipeline, enhancing efficiency and reliability. Terraform is utilized for infrastructure as code, providing a scalable and maintainable architecture. End users interact with the implemented solution through an API Gateway, ensuring a standardized and secure interface.
Pipeline Details along with Architectural Diagrams: The CI/CD pipeline is orchestrated through Gitlab, seamlessly integrating Sagemaker model deployment. The pipeline encompasses stages such as source, build, staging deploy, test staging, and prod deploy. Architectural diagrams illustrate the flow of data and processes, showcasing the integration points between Gitlab, Sagemaker, API Gateway, and other AWS resources.
Platform Details: The entire infrastructure is hosted on Amazon Web Services (AWS), leveraging its services for compute, storage, and machine learning capabilities.
Additional Documentation Details:
- Security Measures: Outline the security protocols implemented to safeguard the multi-account environment and API Gateway interactions.
- Terraform Configurations: Provide detailed Terraform code snippets to reproduce the infrastructure.
- AWS Service Integration: Specify how AWS services are utilized within the architecture.
- Model Monitoring: Detail the model monitoring and logging mechanisms in place for continuous model health assessment like model drift, model bias, data drift, data bias.
- Scalability Considerations: Discuss how the architecture accommodates future scalability requirements.
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