Many start-ups are based on AI / ML competence and require this expertise across the organization. In established enterprises, AI / ML is fast becoming pervasive across the organization given the disruption from start-ups and customer expectations. Depending on the size and level of regulation in their respective industries, machine learning activities might be embedded within existing technology teams or dedicated “horizontal” teams might be responsible for them.
ML activities that people readily recognize are the ones performed by data scientists, data engineers and the like. However, there are other business and technology teams that are essential to enable ML development. Given the potential bias and ethics implications with business decisions made by AI / ML, governance to ensure risk and regulatory compliance will be required too. In this blog, I will cover AI / ML lifecycle along with the functions and departments in an enterprise that are critical for successful ML adoption.
AI Model inventory: There is an increasing regulatory expectation that organizations should be aware of all AI / ML models used across the enterprise to effectively manage risks. This McKinsey article provides an overview of risk management expected in banking industry. As an organization embarks on creating AI / ML development process, a good starting point is to define what constitutes an AI model to ensure common understanding across the organization and create a comprehensive inventory.
Intake and prioritization: To avoid indiscriminate and inappropriate AI / ML development and use, it is important that any such development go through an intake process that evaluates risk, regulatory considerations and return on investment. It is a good practice to define certain org wide expectations and preferable to federate the responsibility for agility.
Data Management: Once an AI Model is approved for development, business and technology teams work together to identify required data, secure them from different data sources across the organization and convert them into feature set for model development.
- Data Administrators manage various data sources, which typically are data lake (Apache Hadoop implementation) or warehouses (like Teradata) or RDBMS (like SQL Server / Oracle).
- Data Engineers help with data preparation, wrangling, munging and feature engineering using a variety of tools (like Talend) and makes feature set available for model development.
Model Development: Data Scientists use AI / ML platforms (like Anaconda, H2O, jupyter) to develop AI models. While model development is federated in most enterprises, AI / ML governance requires them to adhere to defined risk and regulatory guidelines.
Model Validation: An Enterprise Risk team usually validates models before production use, particularly for ones that are external facing and deemed high risk.
Deployment & Monitoring: Technology team packages approved models with necessary controls and integrated into appropriate business systems and monitors for stability and resilience.
Enterprises strive to automate the entire lifecycle so that focus can be on adding business value effectively and efficiently. Open Source platforms like AirFlow, MLFlow and Kubeflow help automate orchestration and provide seamless end to end integration for all teams across AI / ML lifecycle.