Personas for an ML platform - Build a Secure Enterprise Machine Learning Platform on AWS

Personas for an ML platform

Building an enterprise machine learning platform requires the collaboration of different cross-functional teams such as data scientists, cloud engineering, security architecture, data engineering, and audit and governance. The different personas from the different teams all contribute in the build-out, usage and operations of an ML platform, each having a different role and responsibilities. This document focuses on the following personas in an enterprise:

  • Cloud and security engineers — In most organizations, cloud engineering and security engineering teams are responsible for creating, configuring, and managing the AWS accounts, and the resources in the accounts. They set up AWS accounts for the different lines of business and operating environments (for example, data science, user acceptance testing (UAT), production) and configure networking and security. Cloud and security engineers also work with other security functions, such as identity and access management, to set up the required users, roles, and policies to grant users and services permissions to perform various operations in the AWS accounts. On the governance front, cloud and security engineers implement governance controls such as resource tagging, audit trail, and other preventive and detective controls to meet both internal requirements and external regulations.

  • Data engineers — Data engineers work closely with data scientists and ML engineers to help identify data sources, build out data management capabilities, and data processing pipelines. They establish security controls around data to enable both data science experimentation and automated pipelines. They are also responsible for data quality and data obfuscation management.

  • MLOps engineers — MLOps engineers build and manage automation pipelines to operationalize the ML platform and ML pipelines for fully/partially automated CI/CD pipelines, such as pipelines for building Docker images, model training, and model deployment. They utilize different services such as pipeline tools, code repository, container repository, library package management, model management, and ML training and hosting platform to build and operate pipelines. MLOps engineers also have a role in overall platform governance such as data / model lineage, as well as infrastructure monitoring and model monitoring.

  • Data scientists and ML engineers — Data scientists and ML engineers are the end-users of the platform. They use the platform for experimentation, such as exploratory data analysis, data preparation and feature engineering, model training and model validation. They also help analyze model monitoring results and determine if the model is performing as expected in production.

  • IT Auditors — IT auditors are responsible for analyzing system access activities, identifying anomalies and violations, preparing audit reports for audit findings, and recommending remediations.

  • Model risk managers — Model risk managers are responsible for ensuring machine learning models meet various external and internal control requirements such as model inventory, model explainability, model performance monitoring, and model lifecycle management.