Data architecture for AI
Data is an essential ingredient for any AI scenario. One of the key elements for the success of an AI project is the data architecture – how to bring data together, store and process it, bring the results back, and integrate the insights and actions back into the applications. The following are some of the typical challenges of the data architecture for AI:
- Data is located across different data sources based on different formats, systems, and structured and unstructured data types.
- Data integration and consolidation require a common data model.
- Replicating data involves how to address data privacy and data protection concerns, as well as other compliance requirements.
- The data platform provides data for the AI execution engine and also needs to address data ingestion, data storage, and data lifecycle management.
- AI requires metadata such as labeling for supervised learning.
- AI lifecycle events can be tightly coupled with the...
