Time & Cost Reduction in End-to-End Machine Learning Pipeline
Developed an end-to-end ML pipeline using Amazon SageMaker and AWS tools to reduce development time, cost, and manual effort through automation and reusable templates.
About This Project
The project focused on reducing the time and cost associated with end-to-end machine learning pipeline creation using Amazon SageMaker. This involved three key initiatives: adapting SageMaker JumpStart to enable rapid development of high-level ML solutions within hours, implementing organizational templates to streamline the creation of mid-level solutions within days, and revamping MLOps CI/CD templates to minimize repetitive work and manual intervention in pipeline development. By leveraging tools and technologies such as AWS SageMaker, AWS CloudFormation Templates (CFT), AWS Service Catalog, Amazon S3, AWS CodeCommit, and AWS CI/CD frameworks, these initiatives significantly reduced development time and costs, enhancing the productivity of data science teams and ensuring efficient, automated pipeline creation.
Technologies Used
Key Highlights
- Automated end-to-end ML pipeline creation using Amazon SageMaker and AWS CI/CD tools.
- Reduced development time and cost through reusable templates and streamlined workflows.
- Enhanced productivity and scalability for data science teams via MLOps best practices.
- Integrated AWS services such as CloudFormation, S3, and CodePipeline for seamless deployment.