Description: Presently, the Data Science services section only accommodates Customer AI, primarily utilized for generating propensity scores for diverse profiles. However, it falls short in empowering customers to deploy their personalized machine learning models. This limitation necessitates a reliance on alternative tools, hindering the full utilization of data resources.
Why is this feature important to you: The integration of this feature is crucial as it opens avenues for deriving tailored insights from data, aligning precisely with the unique requirements of each customer. The absence of the ability to deploy individual machine learning models constrains the potential utility of the available data, impeding the generation of valuable, customer-specific insights.
How would you like the feature to work: We propose the enhancement of the RT-CDP (Real-Time Customer Data Platform) by incorporating the capability for users to deploy their personalized machine learning models within the Data Science framework. This can be facilitated by introducing a new, user-friendly interface for the data science workspace, akin to existing solutions like Treasure Data. Enabling seamless integration and deployment of custom models would empower users to extract more meaningful and targeted insights from their data.
Current Behaviour: At present, the platform is limited to the out-of-the-box (OOTB) Customer AI Intelligent services. While these services provide valuable functionalities, the absence of an option to incorporate individual machine learning models into the platform confines users to the predefined capabilities. This creates a gap where users cannot fully leverage their data for more specific and nuanced analyses. The current system lacks the flexibility required to accommodate the diverse machine learning models that users may want to deploy.
