MLOps and LLMOps: Run AI Models Like Real Software
MLOps & LLMOps: leave with a tracked experiment, registered model/prompt versions, a deployed endpoint and a rollback drill.
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- Bring your own work — code, campaign, report or plan
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What this session would cover
Proposed outline — the mentor finalises the agenda once this topic is scheduled.
- 1Why mlops & llmops matters — the common problem: Nobody can say which model or prompt version is live or roll back when quality drops.
- 2Core concepts in plain language: Experiment tracking, dataset versioning, model registries, training pipelines, model deployment, feature stores
- 3Going further: inference endpoints, model monitoring, data drift, model drift, retraining, prompt versioning
- 4Framework walkthrough: NIST AI Risk Management Framework, OWASP Top 10 for LLM Applications, Model Cards
- 5Practical workflow, built live: A tracked experiment, registered model/prompt versions, a deployed endpoint and a rollback drill.
- 6How to measure it: Regression-eval pass rate, Drift alerts, Security findings, Incident count
- 7An illustrative case (a fictional example, not a client result), then live Q&A on your own situation
Who it's for
- • Students and freshers entering tech
- • Working developers and engineers
- • Tech leads and architects
You'd leave with
- A tracked experiment, registered model/prompt versions, a deployed endpoint and a rollback drill.
- A working understanding of NIST AI Risk Management Framework and OWASP Top 10 for LLM Applications
- A short list of measures to track: Regression-eval pass rate, Drift alerts, Security findings
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