Considerations with Combining AI and Data

An interesting and lengthy piece in InfoQ on key issues with the construction and delivery of AI solutions. Use of data ontologies are mentioned,  a good idea.   Their key takeaways are instructive:

- Machine learning in turn is frequently fueled by big data but can also be fueled by traditional data sources.

- No matter what the scope is, we have to select data that is appropriate to the domain of the problem space

- Information from highly diverse sources needs to be parsed, curated, packaged, contextualized, and componentized for consumption by users or ingested by systems.

- While machine algorithms play an important role in both the preparation of data and interpretation of user intent, these types of applications require a significant amount of knowledge engineering to be successful.

- Thinking about data as a service and the platform as an orchestration layer between business problems and technology solutions can help organizations achieve dramatic improvement in data scientists productivity. .... " 

Much more at the link.