Building an AI Root Cause Analysis Prototype

1. 1Train a DistilBERT model on customer contact data
2. 2Preprocess customer contact drivers into suitable input formats
3. 3Fine-tune the DistilBERT model for root cause hypothesis generation
4. 4Evaluate the performance of the model using relevant metrics
5. 5Integrate the model into a larger prototype for root cause analysis

Machine learning engineers and data scientists can benefit from this article to improve their root cause analysis capabilities, while product managers can use this to inform product decisions

💡 DistilBERT can be used to generate root-cause hypotheses from customer contact drivers

Build an AI root cause analysis prototype using DistilBERT! #AI #RootCauseAnalysis