Data scientists have focused on one-off projects and relied on discrete analytic tools without sufficient operationalization and governance.
Goals and Objectives
Institute ModelOps practices that provide the enterprise with operationalized, scalable, and extensible yet governed capabilities to support the full AI/ML/analytics life cycle on an ongoing basis.
AI/ML development platforms and data science workbenches, as well as discrete tools for deep learning, transfer learning, NLP, Q&A systems, image/voice/audio/video analysis, speech recognition, conversational AI platforms for building digital assistants, and corresponding third-party services
Use Case Summary
AI/ML and other advanced analytics model development and testing, model deployment, continuous model monitoring and enhancement in an environment that allows data scientists to collaborate among themselves and with their IT and business colleagues; a broad range of human-computer interaction techniques (voice, touch, gestures, etc.) across in-office and infield situations