Innovate with Ajay: Engineering Production-Ready AI Systems
See how a real AI feature moves from prototype to something you'd actually ship — with a checklist for what “production-ready” means.
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What this session would cover
Proposed outline — the host finalises the agenda once this topic is scheduled.
- 1What separates a working AI demo from a feature you can put in front of real users
- 2Walking one AI feature from prototype to release: inputs, prompt/model layer, output handling and fallbacks
- 3Handling the failure cases: empty, wrong, slow or unsafe model output and what the user sees each time
- 4Cost, latency and rate limits — measuring them before launch instead of discovering them after
- 5Logging, evaluation sets and how to know if a change made the feature better or worse
- 6A “production-ready” checklist you can run against your own AI feature, reviewed item by item
Who it's for
- • Developers who have built an AI prototype and want to ship it responsibly
- • Tech leads deciding whether an AI feature is ready for users
You'd leave with
- A production-readiness checklist for AI features
- A clear picture of the failure modes to design for before launch
- A simple plan for evaluating and monitoring one AI feature
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