After shipping the AI Interview Analyzer on GCP
, I realized that production-ready AI isn’t about adding more models — it’s about orchestrating them efficiently.
This build used:
FastAPI + Whisper for fast audio transcription
RoBERTa + Toxic-BERT + mDeBERTa for tone and competency scoring
Gemini 2.0 Flash for contextual feedback
Compute Engine to handle large audio workloads
It taught me three truths about real ML deployment:
1️⃣ Infrastructure matters more than model size.
2️⃣ Feedback loops make AI useful, not just functional.
3️⃣ Performance visibility (CloudWatch / GCP Monitoring) builds trust.
Full article 👇
🔗 https://dev.to/marcusmayo/building-an-ai-powered-interview-analyzer-on-gcp-31ia
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