Targeted business outcomes
The key business outcome is to adopt optimized processes that can transform your organization's data into business value. Specifically, this strategy can help you achieve the following targeted business outcomes:
Scale machine learning and operations (MLOps) capabilities across your organization and develop an ML platform to support hundreds of data scientists and data engineers to run ML workflows in your organization.
Implement a scalable, secure, cost-efficient, and sustainable infrastructure deployment by using AWS Service Catalog to create an on-demand managed infrastructure with Amazon SageMaker.
Standardize the ML model development and deployment process across multiple teams.
Reduce the technical debt of existing models and create reusable artifacts to speed up future model development.
Reduce idea-to-value time for data and analytics use cases down to 12–16 weeks.
Reduce the creation time for ML use case environments down to 1–2 days.
When you scale the use of advanced analytics in the enterprise, it can take too long to develop and release ML models and solutions into production. By partnering with AWS, you receive guidance and support that can help you rapidly design, build, and launch a modern, secure, scalable, and sustainable self-service platform for developing and productionizing ML-based services to support your business and customers. AWS Professional Services
The collaboration provides the following:
A federated, self-service, and DevOps-driven approach for infrastructure and application code, with a clear route to live that can reduce deployment times from weeks to minutes
A secure, controlled, and templated environment to accelerate innovation with ML models and insights that use industry best practices and bank-wide shared artifacts
The ability to access and share data more easily and consistently across your organization
A modern toolset based on an on-demand managed architecture that can minimize compute requirements, drive down costs, and enable sustainable ML development and operations, with the flexibility to accommodate new AWS products and services to meet ongoing use case and compliance requirements
Adoption, engagement, and training support that enables data science and engineering teams across your organization to be self-sufficient and scale ML products across your organization
