Artificial Intelligence integrated with BangDB for AutoML
BangDB natively integrates AI with Stream, Graph and Document database. This automates, simplifies, and accelerates the AI ops.
AI as native construct of database
Train, test, version, store and deploy models - automate these steps
Train, test, version, store and deploy models - automate these steps
The AI is required for almost all use cases now. It's a common practice to export data to AI silo, train, test, and store there, and then bring it to application for predictions.
This doesn't scale. It lacks automation. And it's hard to manage
BangDB brings AI to the data. The AI is part of the DB construct, therefore available as operation. Therefore, the entire process related to ML ops could be automated.
- This makes it very efficient to deal with AI
- This reduces overall cost by a huge margin
- The overhead of ML ops or dev-ops reduces significantly
AI integrated with the Graph Query
Write Cypher query and it uses AI wherever necessary
Write Cypher query and it uses AI wherever necessary
Graph has natural advantages due to the lay out of the data where context is preserved. There are many use cases where Graph query becomes super-efficient if AI may also be used. Such as Clustering, Similarities, Group leaders etc.
Cypher queries can leverage AI as part of query itself. The user may not have to explicitly define the AI – instead, Graph uses it wherever necessary. It also updates the graph if required with the results for more queries in future
- Fraud detection, Recommendations, Anomaly detections in financial transaction or call records, IOT clustering cases etc. are few examples where this feature scales well
- There is no AI operation overhead here, hence users can simply focus on their use cases. It improves productivity in a significant manner
Resource Server for Large files
BangDB enables users to manage AI related files within DB itself
BangDB enables users to manage AI related files within DB itself
ML/DL deals with large model files, along with several other intermediate or related files which could be text or binary in nature. AI will not be fully integrated unless these files are also available within the database.
To achieve this, BangDB implements S3 (AWS) like feature within core database by enabling the support for large binary files. BangDB calls this BRS (BangDB Resource Service). Therefore, the DB manages these files versions, storage, access, and deployment automatically. Users would have to go out of BangDB for such tasks too which are very critical from the operation overhead reduction and automation perspective
AI integrated with Stream Processing
Continuous predictive analysis of time series events
Continuous predictive analysis of time series events
- Train models on the streaming data. Put it on auto-pilot mode for retrain, version and deploy
- AI as part of event transformation for data enrichment
- AI as part of CEP for pattern identifications
- AI for data filtering, joining with other streams for interesting events
The AI with streams puts the Time-series data processing on steroids. Time-series events with real-time requirements find it highly productive to be able to also integrate with AI for various use cases