The AI/ML lifecycle is a complex, multifaceted process that relies on unique contributions from highly specialized stakeholders in data science, ML/platform engineering, and governance.
We’ve worked with hundreds of stakeholders across organizations at almost every phase of the model lifecycle to inform our understanding of all steps of building and operationalizing machine learning. The full model lifecycle contains six distinct phases, tied together by the overarching goal of model management:
Data Preparation
Train
Validate
Deploy
Operate
Monitor
Download the guide to get a clear understanding for managing the full AI/ML model lifecycle.
Download the Free Guide
Who it's for: Data scientists, ML & platform engineers, business & technical leaders, AI/ML governance & risk management specialists
