A vision of success
To successfully unlock the value of your data, your organization must develop a scalable ML framework that achieves your business outcomes while aligning to the AWS best practices defined in this strategy. Such a framework could help your organization achieve the following:
A self-service process for creating standardized production-level infrastructure for developing advanced analytics, data science-based workloads, and a clear DevOps-driven route to live for those workloads
Use case teams that can enjoy streamlined governance, which reduces time to value while also freeing up valuable technical resources
A data science library to access and share reusable ML templates that can rapidly reduce end-to-end delivery timescales by using templates that can be refined and shared with others
A cloud data reference library (DataHub) that provides metadata on all data located across multiple AWS data lakes, speeding up not only data discovery but time to access the data required to train models
A dedicated DevOps team trained to manage the platform and underlying toolkit, support new feature development, and ensure ongoing compliance with key controls
Real-life use cases delivered through to production-level accounts
A knowledge transfer between the project team and the receiving business line teams (spoke teams), which enables business teams to self-manage continued development and the route-to-live process
Completion of initial engagement and adoption activities with a targeted group of business teams
Hundreds of classroom-based AWS training sessions delivered to support adoption
