1. realtime features, timeplus can help build realtime features with realtime data processing capabilities
2. realtime data context, for AI LLM that need fresh data context to do a better job, timeplus can be used to process and service those realtime data for it
3. realtime inference, in those cases where user want to do realtime inference, leveraging those UDF, user can combine the realtime data with model inference in one single box, greatly simplify the overall ML architecture
What are the real-use cases and pain points Timeplus solves in AI / ML ?
1. realtime features, timeplus can help build realtime features with realtime data processing capabilities
2. realtime data context, for AI LLM that need fresh data context to do a better job, timeplus can be used to process and service those realtime data for it
3. realtime inference, in those cases where user want to do realtime inference, leveraging those UDF, user can combine the realtime data with model inference in one single box, greatly simplify the overall ML architecture
any blogs i can reference and compare to other solutions, what are the key differentiations ?