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Production AI Radar
Model packaging with BentoML
Package classical ML and LLM runners as versioned deployable services.
TrialMLOpsNew
- Why this ring
- Bridges notebook → API gap without jumping straight to KServe complexity.
- Production risk if ignored
- Ad-hoc Dockerfiles per model diverge - no shared health checks or metrics.
- Typical effort
- weeks
- Medium FinOps impact
Use cases
- Classical ML APIs
- Multi-model services
- Unified deploy artifact
Adoption steps
- Bento one model
- Standard health/metrics
- Promote via registry
- Compare KServe later
Related tools
In your assessment
Serving packaging standard + SLO review