Aman Khan
where else to find them
github:amank94
linkedin:amanberkeley
what they talk about
all 5 in the filters
where they keep going back
no series more than once — all 3 in the filters
on a stage with
who talks about the same things
- Aparna Dhinakaran5 talks
- Dat Ngo3 talks
- Rajesh Gupta3 talks
- Shishir G Patil5 talks
- Hamza Farooq4 talks
- Hila Fox4 talks
| title | conference | date |
|---|---|---|
| Shipping AI That Works: An Evaluation Framework for PMs | Observe 2026 | 2026-06-04 |
Concrete claim: a reproducible eval loop (dataset → prompt playground → experiment → LLM-as-judge → human labels) surfaces real gaps. Demo builds a multi-agent… | ||
| Engineering Better Evals: Scalable LLM Evaluation Pipelines That Work | Observe 2026 | 2026-06-04 |
Practical recipes: build evals that grade actual claims, not topics — combine LLM-as-a-judge with human-in-the-loop and cheap heuristics; use encoder-only… | ||
| Feature Store Observability: What it is, and why it matters | Feature Store Summit 2022 | 2022-10-11 |
Argues that feature stores need active observability not just alerts and claims a survey of ~1,000 companies shows incidents take about a week to detect and… | ||
| Panel 02 - Data Quality & Feature Stores | Feature Store Summit 2022 | 2022-10-11 |
Panel lays out concrete checks and tools: three failure modes—training data quality, serving data quality, and online-offline consistency—with solutions like… | ||
| How Spotify Does ML At Scale | TWIMLcon: AI Platforms 2021 | 2021-01-19 |
Spotify reports building an ML platform since 2017 that centralizes tooling—Jukebox feature store, Kubeflow Pipeline (skf/qflow), Salem model serving and ML… | ||
| nothing matches | ||